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Map of HPC systems
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Collected in this website we have a compilation of the software modules that have been documented by the E-CAM community within the four target areas of E-CAM. E-CAM software modules are developed via three main activities: the work of post-docs in the context of pilot projects with industrial partners; the work of the participants at Extended Software Development Workshops (ESDWs) and the work of our research software engineers on transversal software development projects of interest to the four scientific areas.
The ERA Chair Team in Mathematical Statistics and Data Science "SanDAL" organises a Workshop on Data Science on the 13-14 October 2020 at the University of Luxembourg on Belval campus. The objective of the workshop is to present some research carried out within our university in life sciences, physics, economics, computer science and mathematics and that is related to data science either as a tool or as an area of research. Speakers are invited to present the type of data they process, the information they want to infer from them, and the tools they use. The workshop will leave time for discussion and interaction between the researchers in order to spur collaborations and interdisciplinarity.
The focus of this 2 days course is on shared memory parallelization with OpenMP for dual-core, multi-core, shared memory, and ccNUMA platforms. This course teaches OpenMP starting from a beginners level. Hands-on sessions (in C and Fortran) will allow users to immediately test and understand the OpenMP directives, environment variables, and library routines. Race-condition debugging tools are also presented.
Description
Se proporciona la formación necesaria para el análisis de datos procedentes de técnicas de Next Generation Sequencing, centrada particularmente en su aplicación al estudio metagenómico de muestras de diversos ambientes y emplear la supercomputación en la recopilación y ensamblado de los fragmentos de ADN secuenciados, así como su posterior anotación y análisis.
Type of methodology: Combination of lecture and hands-on, Self learn
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes for all
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
La necesidad de analizar grandes cantidades de datos para el procesamiento de datos metagenómicos requiere de uso infraestructuras de computación de alto rendimiento como SCAYLE.
Type of methodology: Hackathon, Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge.
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Centrada particularmente en su aplicación al estudio metagenómico de muestras de diversos ambientes y emplear la supercomputación en la recopilación y ensamblado de los fragmentos de ADN secuenciados, así como su posterior anotación y análisis.
Type of methodology: Combination of lecture and hands-on, Self learn
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
La primera parte está enfocada en métodos de ensamblaje híbridos, combinando diferentes tipos de secuencias, especialmente secuencias largas y cortas. En la segunda parte se trabajará en profundidad con los resultados de binning, para poder recuperar con gran precisión genomas muy completos a partir del metagenoma. Se enseña cómo depurar los bins, cómo completarlos, y cómo combinar resultados de diferentes métodos. La tercera y última parte se centra en el análisis estadístico de los resultados, usando R para obtener asociaciones entre abundancias de taxones/genes/rutas metabólicas y tipos de muestra, por ejemplo condiciones ambientales o parámetros clínicos.
Type of methodology: Combination of lecture and hands-on, Self learn
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
About half the course is intended to teach basic (both theoretically and in practice, by writing several parallel codes) notions of parallel programming (MPI), and to make students aware of how to measure the scalability of their parallel codes.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Fortran
El Taller tendrá lugar el día 30 de octubre de 2019 en horario de mañana de 11:00h a 14:00h, en el Edificio Área Científica de la UDC, aula 3.01.
Type of methodology: Combination of lecture and hands-on
Contenido
Descripción FinisTerrae II:
- Arquitectura cluster
- Nomenclatura: Nodo/Socket/Core/Hilo
- Descripción del hardware
Acceso:
- Cuenta de usuario: derechos y obligaciones. Portal. Consumos del portal
- SSH: Descripción y programas disponibles. Ejemplos de acceso y subida de ficheros desde
- Linux/Windows/Mac
- Escritorio remoto: Descripción
- PN: Descripción y escenarios de uso
Sistema de colas:
- Qué es?
- Por qué es necesario?
- Límites y Prioridades
- Conceptos y comandos básicos
- Trabajos interactivos
- Directorios de scratch
Aplicaciones:
- Software instalado
- Petición de nuevo software
- Uso de las aplicaciones (modules)
- Ejecución de trabajos simples
El CESGA impartirá una nueva edición del curso «Introducción al Paralelismo» en Santiago de Compostela los días 18, 19 y 20 de noviembre con horario de 10:00h. a 14:00h.
El curso se dirige a profesionales e investigadores interesados en explotar los recursos que nos ofrecen los ordenadores actuales para mejorar el rendimiento de sus aplicaciones.
Temario:
- Introducción a la arquitectura de computadores.
- Introducción al paralelismo, conceptos básicos.
- Tipos de paralelismo.
- Clasificación de Flynn.
- Memoria compartida vs. memoria distribuida.
- Entornos de programación paralela.
- Prácticas básicas con OpenMP e MPI.
Type of methodology: Combination of lecture and hands-on
Participants prerequisite knowledge: Es conveniente tener unos mínimos conocimientos de programación para el adecuado aprovechamiento del curso, pero no es necesaria ninguna experiencia previa de programación paralela.
El taller se imparte el 11 de noviembre en horario de mañana de 10:00h a 13:00h en el CESGA.
Contenidos:
1.- Introducción al cloud.
Opennebula como gestor de cloud
Infraestructura cloud del CESGA
2.- Funcionamiento de Nebula.
Introducción a recursos virtuales
Introduccion a máquinas virtuales
3.- Nebula como recurso.
Funcionamiento en entorno gráfico web
Funcionamiento en entorno de comandos linux
4.- Casos de uso.
Type of methodology: Combination of lecture and hands-on
Participants prerequisite knowledge: Por ser un taller introductorio, no hay prerequisitos expecíficos, aunque sería deseable conocer el funcionamiento básico del sistema operativo linux, al ser este el SSOO que más se emplea en la plataforma cloud.
En este taller se hará una introducción a la nueva plataforma Big Data del CESGA. Esta plataforma ha sido actualizada a Hadoop 3 e incluye también la nueva versión de Spark 2.4.
Type of methodology: Combination of lecture and hands-on
Type of methodology: Combination of lecture and hands-on
Type of methodology: Combination of lecture and hands-on
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Profesor: Dr. Manuel Arenaz, CEO de Appentra Solutions y profesor na UDC.
Contenidos:
Día 1: jueves 10 de diciembre de 2020
- Bienvenida y presentaciones.
- Introducción a la vectorización, el paralelismo y OpenMP para CPU multinúcleo.
- Desarrollo de códigos paralelos con recomendaciones de mejores prácticas.
- Patrones de código de software paralelo: patrones de cálculo, patrones de memoria y patrones de flujo.
- Herramientas de software paralelo: Trainer y Analyzer.
- Paralelización del cálculo de Pi, MATMUL, HEAT.
- Ejercicios para llevar a casa.
- Prácticas con tu propio código.
Día 2: viernes 11 de diciembre de 2020
- Horas de trabajo.
- Demostración de ejercicios de tarea, soporte, preguntas, preguntas frecuentes utilizando herramientas de Parallelware.
Parallelware Trainer y Parallelware Analyzer son herramientas novedosas para el desarrollo de código paralelo C / C ++ / Fortran para CPU y GPU multinúcleo utilizando OpenMP y OpenACC. Diseñados en colaboración con expertos en programación paralela de Computación de Alto Rendimiento (HPC), brindan un enfoque sistemático y más predecible que aprovecha las mejores prácticas de programación paralela y permite al principiante escribir códigos al nivel de expertos.
Appentra presentará un curso práctico sobre vectorización y paralelización utilizando Parallelware Trainer y Parallelware Analyzer, que es una continuación de la “Introducción al paralelismo” impartida por CESGA. El curso está destinado a ayudar a los nuevos programadores y existentes a comprender las mejores prácticas para la programación de CPU multinúcleo con OpenMP que cubre la vectorización y la paralelización de memoria compartida. La lista de temas se muestra en la siguiente tabla.
Durante el curso, las herramientas recopilarán el uso anónimo de qué herramientas y análisis están siendo invocados por los usuarios. Esto es para ayudar al trabajo de desarrollo posterior de las herramientas.
Este curso está dirigido a investigadores, profesionales y estudiantes universitarios interesados en explotar los recursos que ofrecen los ordenadores actuales para mejorar el rendimiento de sus aplicaciones.
La participación es gratuita.
El curso está limitado a 40 asistentes.
Para leer los términos y condiciones del curso:
https://www.appentra.com/wp-content/uploads/2020/11/20201210-TERMS-AND-CONDITIONS-CESGA-COURSE.pdf
REGISTRO:
https://forms.gle/wjWmzQbjvLHZ7nRv6
Nuestro objetivo es ayudar a hacer más ciencia acelerando el tiempo de ejecución de su aplicación y dedicando menos tiempo a codificar. Si su equipo de desarrollo tiene poca o ninguna experiencia en programación paralela, CESGAHACK5 es para usted.
Durante la semana, el evento está estructurado para garantizar que la mayor parte del tiempo se dedique a trabajar en su propio código, con la ayuda de mentores expertos. Los participantes tendrán acceso al supercomputador Finisterrae II del CESGA.
También usaremos Parallelware Analyzer y Parallelware Trainer para ayudarlo a identificar los patrones paralelos en su código y aprender cómo implementar esos patrones de manera eficiente con OpenMP y / o OpenACC.
Type of methodology: Hackathon
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No necesitas experiencia en programación paralela. Los mentores expertos estarán disponibles para guiarlo durante la semana y, al utilizar Parallelware Trainer, podrá identificar rápidamente oportunidades reales para paralelizar su aplicación. Nuestro objetivo es que los participantes pasen el 95% del tiempo en el hackathon trabajando en su propio código. Nuestros mentores lo ayudarán a usar OpenACC para usar en GPU y / o OpenMP para multiproceso y SIMD. Esto incluirá comprender cuáles son las mejores opciones para su código en particular y si la programación híbrida en paralelo es útil para usted. Es muy recomendable asistir al “Curso práctico de vectorización y paralelización para Finisterrae usando herramientas Parallelware” el 10 de diciembre.
Type of methodology: Hands-on, Lecture
Participants receive the certificate of attendance: if requested
Paid training activity for participants: No, it's free of charge
The course covers the two widely used programming models: MPI for the distributed-memory environments, and OpenMP for the shared-memory architectures. The course also presents the main tools developed at BSC to get information and analyze the execution of parallel applications, Paraver and Extrae.
It also presents the Parallware Assistant tool, which is able to automatically parallelize a large number of program structures, and provide hints to the programmer with respect to how to change the code to improve parallelization. It deals with debugging alternatives, including the use of GDB and Totalview. The use of OpenMP in conjunction with MPI to better exploit the shared-memory capabilities of current compute nodes in clustered architectures is also considered. Paraver will be used along the course as the tool to understand the behavior and performance of parallelized codes. The course is taught using formal lectures and practical/programming sessions to reinforce the key concepts and set up the compilation/execution environment.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Fortran, C or C++ programming. All examples in the course will be done in C.
Attendants can bring their own applications and work with them during the course for parallelization and analysis.
Sessions will be in October 13th-16th and 19th-22nd 2020 from 2pm to 5.30pm with 2 breaks of 15' and delivered via Zoom
More precisely, the course will cover:
- Introduction to earth science fundamentals and modelling;
- Basic usage of an HPC environment: shell, compilers, libraries, file systems, queuing system and parallel computing;
- Build and configure targeted earth science applications with the NMMB/MONARCH chemical transport model and with the EC-EARTH climate model;
- Execute and monitor numerical experiments using a workflow manager;
- Analyse and visualise model outputs with a wide set of tools.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: At least University degree in progress on Earth Sciences, Computer Sciences or related area.
Basic knowledge of UNIX
Knowledge of C, FORTRAN, MPI or openMP is recommended
Knowledge of Earth Sciences data formats is recommended (grib, netcdf, hdf,…)
Knowledge of R and python
This panorama includes the basics of what is behind the main tools: computational mechanics and parallelization. The training is delivered in collaboration with the center of excellence CompBioMed.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: For trainees with some theoretical and practical knowledge. All courses are designed for specialists with at least 1st cycle degree or similar background experience
Also It will provide an introduction about RES and PRACE infrastructures and how to get access to the supercomputing resources available.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Any potential user of a HPC infrastructure will be welcome
Detailed outline:
Introduction to biomolecular simulation
Coarse-grained and atomistic simulation strategies
Automated setup for simulation
HPC specifics: Large scale parallelization, use of GPU’s
Storage and strategies for large scale trajectory analysis
Learning Outcomes: Setup, execute, and analyze standard simulations in HPC environment
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Basic knowledge of structural bioinformatics Basic knowledge of parallelization strategies. Material will be provided during the course, students are welcome to provide their own use cases.
COMPSs is a programming model which is able to exploit the inherent concurrency of sequential applications and execute them in a transparent manner to the application developer in distributed computing platform. This is achieved by annotating part of the codes as tasks, and building at execution a task-dependence graph based on the actual data used consumed/produced by the tasks. The COMPSs runtime is able to schedule the tasks in the computing nodes and take into account facts like data locality and the different nature of the computing nodes in case of heterogeneous platforms. Additionally, recently COMPSs has been enhanced with the possibility of coordinating Web Services as part of the applications. COMPSs supports Java, C/C++ and Python as programming languages.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Programming skills in Java and Python
These two platforms allow to easily store and manipulate distributed data from object-oriented applications, enabling programmers to handle object persistence using the same classes they use in their programs, thus avoiding time consuming transformations between persistent and non-persistent data models. Also, Hecuba and dataClay enable programmers to transparently manage distributed data, without worrying about its location. This is achieved by adding a minimal set of annotations in the classes.
Both Hecuba and dataClay can work independently or integrated with the COMPSs programming model and runtime to facilitate parallelization of applications that handle persistent data, thus providing a comprehensive mechanism that enables the efficient usage of persistent storage solutions from distributed programming environments.
Both platforms offer a common interface to the application developer that facilitates using one solution or the other depending on the needs, without changing the application code. Also, both of them have additional features that allow the programmer to take advantage of their particularities.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Basic programming skills in Python and Java.
Previous attendance to PATC course on programming distributed systems with COMPSs is recommended.
We will see the different options on using Intel’s KNL memory subsystems and systems equipped with Intel’s Optane technology.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Basic skills in C programming.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes.
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Good knowledge of C/C++; Basic knowledge of CUDA/OpenCL; Basic knowledge of MPI, OpenMP
More specifically, the tutorial will:
- Introduce the hybrid MPI/OmpSs parallel programming model for future exascale systems
- Demonstrate how to use MPI/OmpSs to incrementally parallelize/optimize:
- MPI applications on clusters of SMPs, and
- Leverage CUDA kernels with OmpSs on clusters of GPUs
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Good knowledge of C/C++; Basic knowledge of CUDA/OpenCL; Basic knowledge of Paraver/Extrae
More specifically, the tutorial will:
- Introduce the OmpSs@FPGA programming model, how to write, compile and execute applications on FPGAs
- Show the "implements" feature to explot parallelism across cores and IP cores
- Demonstrate how to analyze applications to determine which portions can be executed on FPGAs, and use OmpSs@FPGA to parallelize/optimize them.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge:
- Good knowledge of C/C++
- Basic knowledge of acceleration architectures and offloading models
- Basic knowledge of Paraver/Extrae
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Experience on Linux system administration is required.
This course will provide very good introduction to the PUMPS Summer School run jointly with NVIDIA also at Campus Nord, Barcelona.
You may also be interested in our Introduction to OpenACC course.
In general, we refer to a processor as massively parallel if it has the ability to complete more than 64 arithmetic operations per clock cycle. Many commercial offerings from NVIDIA, AMD, and Intel already offer such levels of concurrency. Effectively programming these processors will require in-depth knowledge about parallel programming principles, as well as the parallelism models, communication models, and resource limitations of these processors.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Basics of C programming and concepts of parallel processing will help, but are not critical to follow the lectures.
This course is delivered by the GPU Center of Excellence (GCOE) awarded by NVIDIA to the Barcelona Supercomputing Center (BSC) in association with Universitat Politecnica de Catalunya (UPC). It will provide very good introduction to the PUMPS Summer School run jointly with NVIDIA - also at Campus Nord, Barcelona. For further information visit the school website.
As an NVIDIA GPU Center of Excellence, BSC and UPC are deeply involved in research and outreach activities around GPU Computing. OpenACC is a high-level, directive-based programming model for GPU computing. It is a very convenient language to leverage the GPU power with minimal code modifications, being the preferred option for non computer scientists. This course will cover the necessary topics to get started with GPU programming in OpenACC, as well as some advanced topics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: BEGINNERS: for trainees from different background or very little knowledge.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: For trainees with some theoretical and practical knowledge
Persistent memory, such as Intel's Optane DCPMM, is now available for use in systems and will be included in future exascale deployments. This new form of memory requires both different programming approaches to exploit the persistent functionality and storage performance, and redesign of applications to benefit from the full performance of the hardware and ensure correctness and data integrity.
This tutorial aims to educate attendees on the persistent memory hardware currently available, the software methods to exploit such hardware and the choices that users of systems and system designers have when deciding which persistent memory functionality and configurations to utilize. The tutorial will provide hands-on experience on programming persistent memory along with a wealth of information on the hardware and software ecosystem and potential performance and functionality benefits. We will be using an HPC system that has compute nodes with Optane memory for the tutorial practicals.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: C/C++; Python
The course analyzes the role of the programmer, the compiler, the runtime and operating systems when looking for productive programming environments and their efficient implementation. The course also describes the tools required to understand the behavior of parallel applications when executed on current supercomputing architectures (based on a collection of distributed-memory nodes, each one built from current multicore chips and/or accelerators). The course will be very practical with optimization and parallelization assignments using different tools (Extrae, Paraver and Dimemas) and programming models (OpenMP, OmpSs, MPI or CUDA) and insights into their implementation.
Type of methodology: Combination of lecture and hands-on, Self learn
The Supercomputing course of the Crazy about Science programme of the Catalunya La Pedrera Foundation is a course developed by the Barcelona Supercomputing Centre (BSC) aimed at 1st year A-Level students with a scientific vocation interest in STEM careers (Science, Technology, Engineering, Mathematics). This course was created with the idea of introducing the multidisciplinary environment offered by supercomputing where different disciplines come together to solve all kinds of problems. The main objective of this course is to foster scientific vocations and promote the knowledge and education of excellence of young people in Catalonia.
Registration for the Supercomputing course of the Crazy about Science programme will be open from 16 September to 24 October 2019 and among all those enrolled there will be 25 students selected from the 1st year of A-Levels who will participate in the 2nd edition of the course within the 8th edition of the Crazy about Science programme that will take place in 2020.
During the 19 theory-practice sessions (plus the closing session) students will get a closer look at what supercomputers are and how different types of research can be performed using supercomputing. They will also be introduced to the frontier technologies of the computer world, such as Artificial Intelligence and Quantum Computation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Phyton
Due to the capacity and mobility restrictions related to the health emergency, we have converted the activities for schools, institutes and formative cycles into online or classroom activities. The groups of secondary, baccalaureate and formative cycles can request the activity here and will find of complementary information to prepare it here: Visits in the framework of the "We are young women researchers" program for 3rd and 4th grade group have become a complete classroom activity. You can find more information here and arrange a visit through visitesprimaria@bsc.es
Type of methodology: Visits
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: N/A
his course will describe all elements in the system architecture of a supercomputer, from the shared memory multiprocessor in the compute node, to the interconnection network and distributed memory cluster, including infrastructures that host them. We will also discuss the their building blocks and the system software stack, including their parallel programming models, exploiting parallelism is central for greater computational power . We will introduce the continuous development of supercomputing systems enabling its convergence with the advanced analytic algorithms required in today's world. At this point, We will pay special attention to Deep Learning algorithms and its executions on a GPU platform. The practical component is the most important part of this subject. In this course the learn by doing method is used, with a set of Hands-on, based on problems that the students must carry out throughout the course. The course will be marked by continuous assessment which ensures constant and steady work. The method is also based on teamwork and a learn to learn' approach reading and presenting papers. Thus the student is able to adapt and anticipate new technologies that will arise in the coming years. For the Labs we will use supercomputing facilities from the Barcelona Supercomputing Center (BSC-CNS).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Participants prerequisite knowledge: C/C++; Python; MPI; OpenMP; Machine/Deep Learning concepts
Type of methodology: Combination of lecture and hands-on, Self learn
Participants prerequisite knowledge: Basic understanding of parallel architectures, including shared- and distributed-memory multiprocessor systems; useful programming skills of some parallel programming model.
As a graduate of this program you will have a good overview of the main AI techniques and an in-depth understanding of how to apply these techniques in at least one area within multi-agent systems, reasoning, data analytics and natural language processing. These skills are in high demand in the market. You will also have the skills to carry out AI research in academic and R&D environments and to identify how AI techniques can provide intelligent solutions to IT problems in companies and organizations.
Type of methodology: Hands-on; Lecture; Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: N/A
Participants prerequisite knowledge: C/C++; Fortran; Python; HPC tools (e.g. profiler, scheduler); MPI; OpenMP; Machine/Deep Learning concepts; Numerical methods (linear algebra, statistics)
Arm researchers and engineers will introduce the different HPC tools available from Arm including compilers, math libraries, debugging and profiling tools. The hackathon will focus on the benefits of SVE extensions in the context of HPC and ML kernels and applications. The instructors will guide attendees to vectorize codes through compilation, refactoring and intrinsics. The vectorization examples will showcase the features of SVE that help provide higher performance and energy efficiency by increasing utilization of vectors, improving data movement and exploiting in-core acceleration of important compute patterns. To ease this experience, the organizers will provide access to HPC machines with all the required tools already installed.
Type of methodology: Hackathon
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: ;C/C++; HPC tools (e.g. profiler, scheduler); MPI; OpenMP; Master in Computer Sciences or similar
We are broadly looking to engage the Artificial Intelligence, Smart City, Smart Home, Helthcare and IOT communities to evangalize the low-energy optimization techniques that we have developed for these communities during the lifetime of the LEGaTO project. In particular, we will showcase how the heterogeneous low-energy small form-factor hardware could be coupled to an energy efficient runtime to produce one order of energy savings across these use cases. The workshop will consist of invited talks from these communities and will include insights gained during the project for ensuring fault-tolerance and security in addition to energy efficiency.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
You will be able to access the best data and computing resources in Europe and develop internships in leading companies and research groups in areas such as Economy and Finance, Internet of Things, Biomedicine, Environment, Meteorology, Physics and Astronomy, Social Sciences, etc.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
This training is complemented by knowledge and skills related to the management and direction of companies and/or technological projects. The Master in Computer Engineering provides the student with the skills to exercise the profession of Computer Engineer.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, the participants pay the fees, except for some who have a scholarship.
Participants prerequisite knowledge: C/C++; Python; MPI; OpenMP; Machine/Deep Learning concepts
It is aimed at the training of engineers and technicians with industrial experience who want to update their knowledge and recent graduates who have an interest in working in companies that are making the transition to Industry 4.0 from a multidisciplinary way.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Domain-specific background knowledge
Conference aimed at companies, technology centers, students, researchers and innovators interested in current affairs and the daily work of the supercomputing center, cutting-edge technologies and R&D projects.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Presenting both the Foundation and CénitS - The Extremadura Center for Research, Technological Innovation and Supercomputing and its three supercomputers: LUSITANIA, LUSITANIA II and LUSITANIA III. In addition, during the previous talk, the multiple benefits that supercomputing brings to society are exposed, mainly in the field of research, and computaex engineers show some of the research and development projects in which the center has participated since its creation in 2009.
Type of methodology: Guided visits
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Numerical methods for ordinary differential equations, Initial value problem, one-step and multistep methods of Runge-Kutta type, Methods for Solving Ordinary Differential Equations, Applications of ODR numerical methods in physics and biology, Numerical solution of boundary value problems for ordinary differential equations, difference method, shooting method, Partial differential equation: Finite difference method for parabolic, hyperbolic and elliptic problems in 2D, explicit and implicit methods, stability, alternating direction method, Applications of PDR numerical methods in physics and biology, Implementation of numerical algorithms in Matlab and Python
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Biological modeling with ordinary differential equations: the principle of mass balance, mass action rule, scaling and nondimensionalisation, one-component models (Michaelis-Menten kinetics, gene autoregulation), multi-component models (biological switches, oscillators, epidemiology). Modeling with differential equations with delay. Models with spatial component: the reaction-diffusion systems, the spread of epidemics, pattern formation. Stochastic models: probability balance equation, Gillespie simulation algorithm, stochastic models of gene expression.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Markov property, transition probabilities, transition matrix, Chapman Kolmogorov equation, irreducibility of a chain. Classification of states, recurrent states, transient states, null recurrent states and positive recurrent states, periodicity. Existence of stationary distribution, ergodic distribution, necessary and sufficient conditions for ergodicity. Random walks, branching processes, absorbtion probabilities, mean time to absorbtion. Markov reward chains algorithms and Markov Chain Monte Carlo.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Graphical input and output devices. Computer graphics basics: half-toning, font generation, surfaces tessellation, clipping and intersections, rasterization, area filling. Specialized data structures and object representation. Winged edges and half-edges, DCEL, meshes, B-rep, sweeping, CSG, implicit functions and F-rep. Spatial subdivision techniques, wavelets, procedural, deformable, and multiresolution techniques. Data fitting. Object reconstructions.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Signals, systems and Discrete Fourier Transform. Z-transformation, impulse response, filters with finite and infinite impulse response. Discrete orthogonal transformations, PCA. Evaluation of the spectrum, correlation models of the image. Human visual system, color systems. Enhancement of the image: contrast, dynamic range, noise reduction, edge detection. Reconstruction of the image: homomorphic systems, reduction of additive noise, reduction of multiplicative noise. Spectral analysis, reduction of combined noises, reduction of noise depending on the signal. Image interpolation: median, mean, spline methods, convolution interpolation, polynomial interpolation, interpolation by the discrete orthogonal transformations. Image segmentation. Losless image encoding – the principle and basic methods. Lossy image encoding – the principle and basic methods. Some problems connected with the errors of the transmissed encoded image.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
The role of classification, feature articles and Syntax Notation. Selection and pretreatment symptoms. Selection and pretreatment symptoms. Bayesian decision theory, discriminatory and divisive functions hypersurface, the criterion of the minimum error. Decision trees. Discriminant analysis, linear classifier. Mechanisms of support vectors (SVM). Neural networks. Uncontrolled classifiers. Hidden Markov models. Quality rating classification. Syntactic recognition, inference grammar. Special types of grammar.
Type of methodology: J
Participants receive the certificate of attendance: M
Paid training activity for participants: N
Participants prerequisite knowledge: AA
Problems and algorithms. Basic computational models and complexity measures. Complexity classes, their fundamental characteristics and hierarchies. Reduction and completeness in the complexity classes. NP-complete problems. Methods of solving (computationally) hard problems – deterministic methods, heuristics, approximation algorithms, probabilistic algorithms.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Multivariate distributions: multivariate normal distribution, Wishart distribution, Hotelling’s distribution, Wilk’ distribution. Multivariate linear models. Multivariate regression model. Multivariate analysis of variance, the case of one factor, the case of two factors. Analysis of covariance. Qualitative and quantitative factors.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Some properties of matrices. Linear model: the Gauss-Markov theorem, confidence ellipsoids, confidence bounds, testing submodels. Nonlinear regression models: geometrical interpretation, linearization of models, iterative methods of computation of least squares estimates, the consistency and asymptotic normality of estimates.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Bayes theorem, properties of the posterior probability, I-divergence and the information contained in an experiment, non-informative priors (different approaches), conjugate priors, MCMC methods, statistical decision rules, Bayes estimators in general and in particular in linear models, Bayes conception of testing hypotheses and of interval estimation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Orientation in the SAS environment, reading the data files of various formats, validation and data cleansing, data filtering and creation of derived variables, joining multiple data files, creation of reports, saving outputs, chosen basic procedures: syntax, basic settings (options)
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Introduction (definitions), Absorption and emission coefficient, Transfer equation, Radioactive equilibrium, Grey atmosphere, Continuum absorption coefficient, Model atmosphere (hydrostatic equilibrium, temperature distribution, …), Line absorption coefficient, Behavior of spectral lines, Chemical analysis, Stellar rotation, Turbulence in stellar atmospheres.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Plasma in the universe, General properties of plasma, Motion of charged particle in electromagnetic fields (uniform, nonuniform, static, time-varying), Drifts, Magnetic mirror effect, Adiabatic invariants, Boltzmann equation and its moments, Macroscopic transport equations.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Work with Python libraries such as installation, basic set-up, input parameters, usage of functions, programming own scripts. Work with FITS images covering image calibration, segmentation and astrometric reduction. Calculation of object’s ephemerides, coordinates transformation, own scripts development. Usage of Python libraries Python libraries AstroPy, SciPy, NumPy, matplotlib, rebound, SourceExtractor, and sgp4.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Curved space-time, derivation of Einstein equations and their geometrical meaning, Exact solutions of the equations: expanding Universe and cosmological models, spherically symmetric stars and black holes, Linearized gravitation, gravitational waves, Consequences of the positivity of energy: enlargement of black hole horizons, existence of singularities in space-time.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge:Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Assimilation cycle, governed atmospheric equations, map projections, generalized vertical coordinate, barotropic model, integration of atmospheric model, Energy and momentum conservation, available potential energy, atmospheric oscillations, sound speed, surface and internal gravity waves, orographic waves, mixed inertial and gravity waves, Rossby waves, sensitivity of atmospheric model to the initial distribution of mass and velocity fields, transformation of energy.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Central European climate sub-system, climate forming factors and processes and their interactions. Climatic patterns for selected climatic elements. Climate and its peculiarities in the individual Central European regions. Dynamic climatology. Climatic classifications for Central Europe. Climate and mesoclimate of the atmospheric boundary layer and aeroclimatology. Climatic normals and characteristics of selected Central European cities and in Slovakia at changing climate forming conditions.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Elementary terms. Clausius – Clapeyron equation. Condensation nuclei. Vertical atmosphere velocities computation methods. Eckman spiral, water vapor condensation in the surface layer of the atmosphere. Thermodynamic conditions of the fog creating. Convective clouds. Microstructure and physical cloud processes. Precipitation formation theory. Physical conditions of the rainfall process in the surface atmospheric layer. Precipitation measurement failures. Physical aspects of the snow cover formation and its variation. Electrical and optical features of the clouds and precipitations. Artificial stimulation of cloud and rainfall.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge:
Cell, biopolymers, proteins – structure and function. Cell electric properties. Voltage gated channels and action potential formation. Intracellular signaling. Lipid transport and hormonal regulation. Synaptic transmission in I) neuromuscular junction, II) in CNS. Cytoskeleton. Muscle cell physiology: I) Electric activity of skeletal muscle cells, II) Electric activity of cardiac muscle cells. Secretion, blood circulation, immune system, cancer research. Introduction to ecology and system dynamics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Industry 4.0, Big data, Cloud computing, virtual reality, ehealth, GDPR, Visible Human Project, Human Genome Project.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: ) Numerical methods (linear algebra, statistics); Domain-specific background knowledge
- Introduction to the biomechanical concepts.
- Biomechanics of cell membrane and form of cell.
- Biomechanics of human tissues in organism.
- Human locomotion – system of bone muscles.
- Thermomechanics of muscle contraction.
- Active motion of joints.
- Forces on the skeleton.
- Visco-elestic properties of body liquids.
- Heart as a pump machine.
- Hearing biomechanics.
- Breathing mechanics.
- Biomechanics of digestion tract.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Interactions of particles with matter. Base of experimental and theoretical microdosimetry. Applications of microdosimetry in biology (radiobiology, radiotherapy, radiation protection). Survival Curve and its Significance. Theories and models for cell survival. Radiation effects of particles with high linear energy transfer. Radiation exposure from natural background and other sources.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Sources of electron and ion beams, electrostatic, magnetostatic lenses, charhe particles monochromators and analysators, time of flight, magnetic, dynamic mass spectrometers, interpretation of mass spectra.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Mathematical statistics, elements of information theory, general formalism of quantum statistical physics, numerical methods, variation principles, phase transitions, spin models, kinetic equations, transport phenomena, theory of fluctuations, random processes.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Spontaneous symmetry breaking, generalized rigidity, Goldstone modes, topological defects. Quantum magnetism. Superfluidity: basic experimental facts, properties of condensate, Bogoliubov theory. Superconductivity: basic experimental facts, effective model, BCS theory.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Central processing unit (internal structure, registers, ALU). Instructions (format, variety, arguments). Communication with peripheral devices, interfaces, drivers. Programming (machine code, assembler). Command overview with respect to different applications. Side effects, program debugging. Typical machine code structures.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
General principles of nuclear reactions (NRs), reaction kinematics, the role of orbital momentum, rotational potential, types of NRs, reaction cross-section, elastic and inelastic scattering, neutron-induced NRs, nucleon transfer reactions, deep inelastic reactions, optical model, reactions of gamma quanta, compound nucleus reactions, heavy-ion reactions, fission reactions, applications of NRs, thermonuclear reactions, synthesis of superheavy nuclei, nuclear reactions at relativistic energies.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Describing the data, theoretical distributions, errors, estimations – likelihood function, basic estimators, maximum likelihood, the method of moments, least squares method, chi-square distribution, Probability and confidence – basic terms, mathematical probability, Bayesian statistics, confidence level, binomial confidence intervals, Poisson confidence intervals. Taking decisions – hypothesis testing (significance, power, Neyman Pearson test), interpreting experiments, the null hypothesis, binomial probabilities, goodness of fit, ?2 test, the run test, the Kolmogorov test. Two sample problem, matched and correlated samples. Ranking methods – nonparametric methods, Mann-Whitney test, matched pairs, Wilcoxon`s matched pairs signed rank test, measures of agreement – Spearmen`s correlation coefficient, concordance. Usage of computers for data processing and software features.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Approximation by rational functions, approximation by trigonometric functions, solution of linear algebraic equations, solution of nonlinear equations, interpolation and extrapolation, numerical integration, special functions (gamma, beta factorials), random numbers, minimization and maximization of functions. Error propagation. Fourier transformation. Ordinary and partial differential equations.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Why we need accelerators. Linear electrostatic accelerators. Linear resonance accelerators. Cyclic accelerators: cyclotron, fazotron, microtron, betatron, synchrotron, synchrofazotron. Strong focusation. Description of particle trajectories in the accelerator, stability condition. Extracted beams. Colliding beams. Accelerating and accumulating facilities. Colliders. Beam cooling techniques. Application of accelerators in different fields of science, medicine and industry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basics theory of nucleosynthesis, primordial, anthropogenic and cosmogenic nuclides. Principles of nuclear radiometric methods, dating, erosion study, catastrophic events and their investigation by nuclear methods. Position of the Earth in the Solar system. Isotopes and their applications in Solar system formation chronometry. Space, chemical elements in it and their abundances in various objects of Solar system.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Nucleon-nucleon interaction (one-boson exchange potential), phase analysis of NN interaction, G-matrices (Bethe-Goldstone equation), Slater determinant, second quantization, Hartree-Fock theory, description of excited states of nucleus, Tamm-Dancoff approximation, nuclear ground state correlations, random phase approximation (RPA), RPA equation, sum rules, proton-proton, neutron-neutron and protron-neutron pairing interactions, Hartree-Fock Bogoliubov transformation, Barden-Cooper-Schrieffer (BCS) equation, quasiparticle RPA, giant resonances (Gamow-Teller, isobaric analogue states), many-body Green functions of beta transitions, many-body studies within schematic models (Lipkin model, SO(5) model), Monte-Carlo shell model S-matrix and its expansion, mean field theory, Brueckner G-matrices, Walecka model.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to Quantum Field Theory, Free field, Interacting fields, Functional methods .
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Theory of free electron-positron and electromagnetic field, Feynman diagrams in QED, electron-positron annihilation to muons and Compton scattering.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Dynamics of the Universe, physical processes in the early Universe, anisotropies of the cosmic background radiation and the origin of galaxies.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basic terms of the probability theory and mathematical statistics. Important distributions. General scheme of Monte Carlo methods. Sampling of distribution functions. Specific methods for sampling of irregular distributions. Stochastic processes. Imitation of physical process. Structure of transport equation for hadronic and electromagnetic cascade. Solution of transport equation using Monte Carlo. Basic scheme of GEANT package. Definition of materials, medii, geometry of experiment. Volume, subvolume and their positioning. Detector response. Storing of information in data structure. Simulated physical processes and their control. Passage of particle through volume, detector and set. GEANT data structures. Graphics. Interactive GEANT.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Processes and threads. Purpose of programming with processes and threads. Life cycle and scheduling of processing and threads in operating systems. Examples of thread usage. Creating and terminating threads. Thread synchronization. Shared variables, critical sections. The problem of mutual exclusion and possibilities of addressing it. Semaphores, mutexes, condition variables. Pairwise simulations of different synchronization mechanisms in computer systems. Threads in UNIX systems. Threads in Java. Monitor. Thread safety.. Deadlock, livelock, polling. Correctness of multithreaded programs. Efficiency of multithreaded programs. Parallel scientific computing with shared memory. Related information technologies. Parallel programming.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
symmetric ciphers (block and stream ciphers), asymmetric ciphers, underlying problems for asymmetric constructions, hash functions, message authentication codes, digital signatures, passwords, secret sharing schemes, cryptographic protocols and related attacks, zero-knowledge proofs.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basic concepts from molecular biology, algorithms and machine learning. Sequencing and assembling genomes. Gene finding. Sequence alignment. Evolutionary models and phylogenetic trees. Comparative genomics. RNA structure. Motif finding and gene expression analysis. Protein structure and function. Selected current topics. Students of computer science programs will focus on computer science methods and mathematical modeling of the covered problems. Life science students will focus on understanding and correct application of these methods on real data.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Definition of probabilistic model and basic properties of probability, conditional probability, Bayes theorems, random variables, random vectors and their characteristics, limit theorems, introduction to Markov chain theory, probabilistic theory of information, regression model with normally distributed errors, introduction to theory of parameter estimation and statistical hypothesis testing.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Supervised machine learning (linear and generalized linear regression, neural networks, classification with support vector machines, kernel methods, discrete classifiers). Machine learning theory (statistical model of machine learning, bias-variance trade-off, overfitting and underfitting, PAC learning, VC dimension estimates). Unsupervised machine learning (clustering, self-organizing maps, principal component analysis). Reinforcement learning. Ensemble learning (bagging, boosting).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basic terminology: trees, bipartite graphs, graph and labyrinth search. Eulerian graphs. matchings in graphs, König's theorem, Hall theorem and its corollaries. measuring of graph connectivity. Menger's theorem, Planar graphs, Euler's theorem. Kuratowski's theorem. Graph coloring: some NP-hard problems, greedy algorithm. Brooks' theorem. Vizing's theorem. Coloring of planar graphs. Flows, Ford–Fulkerson algorithm and its applications. Integer and group flows, relationship to coloring. Hamiltonian graphs. Chvátal's theorem. Random graphs, probabilistic models, properties of random graphs.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
users, groups, passwords. access permissions for files and directories, file system structure. character and block devices. special file system objects (symlink, pipe).mounting and unmounting of file systems to the directory hierarchy (mount, umount, /etc/fstab). creating file systems. system startup and shutdown - /etc/inittab, runlevels. job scheduling (cron, at, batch). TCP/IP configuration (ifconfig, route). network services (/etc/services, /etc/inetd.conf, /etc/protocols, /etc/hosts, ...)..DNS – client (/etc/resolv.conf). DNS – server.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Agents, types of agents, agent properties. Browse - informed strategies.
2. Search - informed strategies. Games.
3. Logical agents, propositional and predicate database knowledge.
4. Inference of the predicate in the knowledge base.
5. Planning.
6. likelihood naive Bayesian classifier, Bayesian network.
7. Bayesian network, exact and approximate inference in Bayesian network.
8. Using Bayesian networks in artificial intelligence. Introduction to the use of probability theory in games.
9. Monte Carlo method in games.
10. The classic theory of time series, time series models.
11. Use of Bayesian networks inference in time series with uncertainty.
12. Markov priocesy, Kalman filter, the use of artificial intelligence.
13. Decision Theory: simple and complex decision-making, decision trees.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Course gives an introduction to designing effective parallel algorithms, starting with PRAM like models, continuing with design techniques up to a survey of effective parallel algorithms in selected areas. The lecture covers: Parallel models (PRAM, parallel nets), basic design techniques of effective parallel algorithms, parallel searching and sorting, parallel graph algorithms, parallel pattern matching and parallel algorithms in planar geometry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Programming paradigms (object oriented, functional, declarative and others). Language constructs and concepts (pattern matching, continuations, closures, lazy evaluation, futures, promises and others). Examples in various programming languages.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Compiler structure, lexical analysis, syntax analysis methods (top-down, bottom-up); syntax-directed translation. Type checking; Run time support. Metalanguage, code generation, computer models, register allocation; program optimization, data flow analysis, loop optimization, local optimizations. Optimizations for particular computer architectures.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Particular parallel models (grammars, automata), Models of computation in the 2nd class, mutual simulations, Parallel computation theses., Complexity classes and problems efficiently solvable in parallel (NC, P-complete problems), Limitations of parallel computations.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to artificial neural networks (NS), NS logical neurons. The digital / analog Perceptron: the concept of learning with a teacher pattern recognition. Linear NS: vector spaces, auto associative memory. Multi-layer perceptron: the method of back propagation error, training and test set, generalization, selection of model validation. Hebb learning without a teacher, feature extraction, principal component analysis. Learning the competition, self-organizing map clustering, topographic display. Hybrid NS: radial-basis-function NS algorithm for training, properties. Recurrent NS: temporal structure in data, models and algorithms for training, echo state networks, recurrent self-organizing maps. Hopfield model: deterministic and stochastic dynamics, attractors in state space, auto associative memory. Deep architecture NS.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Initially, the students met a simple language for writing parallel programs. UNITY (syntax and semantics) Fundamental parallel and distributed architectures as a way for them to map UNITY programs. The list is the logic of allowing express safety and progress of programs and formally prove the correctness of programs. Subsequently they learn the solution of selected problems in parallel and distributed programming (eg. The shortest way, reader-writers problem dinning philosophers, coordination meetings, drinkers philosophers, sorting, Faulty channels, Global snapshots, detected a stable qualities, Byzantine Agreement). Their zones can optionally be spread based on the development in this area.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Particle systems, motion equations of first order integration methods to calculate the speed and position, state vector system, external forces, restrictive conditions - constraints, response forces, particle collisions - plane. Numerical solution of differential equations, Euler method, Runge-Kuta method, stability criteria to select the time step. Lagrange method without networks, modeling and animation point cloud, SPH, deformation. Animation mobility, spline interpolation to animate movement, reparametrisation spline curves by length, and orientation quaternion interpolation of two or more quaternion. Collision detection, Z buffer algorithm, necessary and sufficient conditions when there are two bodies in a collision, parting line, hierarchy envelopes force response (Response Forces). Three phase detection wide, medium and narrow. Dynamics of rigid bodies, equations of motion, velocity, acceleration, angular velocity and angular acceleration, inertia matrix. Procedurárne animation, systems and methods for creating computer animation liquids, fire, smoke. Computer animation in games and in the film industry. Other applications of computer animation with further developments in the field of computer animation using physical effects.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
The computational model in numerical mathematics. Numerical stability and robustness, error analysis. Approximation theory. Numerical algebra. Solving large systems of linear equations. Finding roots of nonlinear equations. Numerical differentiation and integration. Optimization - formulation challenges the foundations of convex analysis, numerical methods used to find minima - Gradient methods. Finite Difference Method and Finite Element Method. Introduction to numerical solution of equations diferencálnych. Libraries of numerical methods and work with them.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to approximation algorithms. Neaproximovateľnosti term. Probabilistic analysis of algorithms and their complexity. Las Vegas and Monte Carlo. Integer linear programming. Overview of a hierarchy of complexity classes. Demonstrations on examples.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Agent (a specific definition). Autonomy and mobility. Receptors, efectors, controller, senzors, aktuators. Agent classification: reactive, deliberative and hybrid agents. Communication among agents: direct and indirect. Representation languages: XML and KIF. Multi-agent system (a specific definition). Communication languages. KQML. Implementation of multi-agent systems. Multi-agent system implemented as a middleware.Implementation within OOP virtual machine. Implementation over SRR model (IPC). Pyramidal Client – Server architecture. Agent – Space architecture. Robustness, decentralization, normalization. Deliberative and non-deliberative robotics. New artificial intelligence. Dekomposition by function and activity. Subsumption architecture. PKA model.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to programming languages, compilers and interpreters, Virtual machine, code, memory management, Abstract syntax trees and other representations, Lexical analysis, Parsing, Namespaces, Algorithms for compiling language constructs, data structures and expressions, Code Generation, Error Handling.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Image representation and processing, color models and transformations among them
2. Morphologic operations with image, contours detection and structural analysis
3. Filters and kernels. Edge operators.
4. Blending, seemless cloning, morphing, inpainting
5. Image segmentation. MeanShift filter. GrabCut. Intrinsic image.
6. Image alignment and registration. Phase correlation. ECC. Image features: SIFT, SURF, BRIEF, ORB
7. Camera and video. Optical flow. Stereovision. Camera calibration.
8. Machine learning: PCA, LDA, eigenimages, SVM, cascade regressor and gradient boosting 9. Object detectors. Hough transform. Haar detector. HOG detector. LBPH.
10. Object trackers. Kalman filter. CamShift. MIL tracker. Motion detector.
11. Usage of deep learning models: Colorization, YOLO detectors, vectorization and recognition, EAST text detector, Tesseract OCR, GOTURN. The used programming languages: Python a C++
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Text Processing. Language Modeling (n-grams), Spelling Correction. Text Classification (Naive Bayes), Sentiment Analysis. Named Entity Recognition (HMM, MaxEnt), Relation Extraction. POS Tagging, Parsing. Information Retrieval. Meaning Extraction, Question Answering.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Equations of motion for ideal fluids. Vorticity. Irrotational flow. Vorticity equation. Equations of motion for viscous fluids. Examples of simple viscous flows. Flows with circular streamlines. Convection and diffusion of vorticity. Gravity waves. Dispersion and group velocity. Surface tension effects and capillary waves. Internal gravity waves. Waves with finite amplitude. Hydraulic shocks and solitary waves. Kelvin--Helmholtz instability. Thermal convection. Centrifugal instability. Theorem on the stability of shear flow. General theorem on the stability of viscous flow. Uniqueness of steady viscous flow. Transition to turbulence.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Heat transfer in the mantle. The cooling of the Earth. The cooling of the Earth’s core as a source of small-scale modes of mantle convection. Hot spots. The heat transferred by plumes. The matter transported by plumes. Dynamics and the shape of mantle plumes. The cooling of oceanic lithosphere as a source of large-scale modes of convection. Plate tectonics, the role of lithosphere. The influence of plates on the mantle convection. An effect of phase transitions in mantle transition zone. The mantle as a dynamic system. The problem of layering of mantle convection. The geochemical properties of mantle. The history of the Earth’s mantle.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Tectonic activity of the lithosphere, stresses in the lithosphere, cohesion, internal friction, Coulomb criterion, Anderson’s fault theory, fault population, structure and rheology of the fault zone, thermodynamics of the fault zone, aseismic motions. Fault surface, initial stress. Initialization of the rupture, modes of rupture propagation, spontaneous rupture propagation and boundary conditions on the fault surface. Friction – microscopic and macroscopic views. Results of laboratory tests. Frictions laws and rupture propagation on the fault. Healing of rupture. Energy budget of the rupture initialization and propagation. Seismic efficiency. Effects of the initial stress, material heterogeneity and geometry of the fault surface. Effect of the pore pressure. Frictional heating. Small and large earthquakes in terms of rupture propagation and seismic efficiency.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Time-frequency analysis, Heisenberg-Gabor uncertainty principle, windows Fourier transform, continuous wavelet transform, marginal distributions (Wigner-Ville) for real seismic signals and their discrete versions, adaptive pursuit methods-orthogonal/non-orthogonal matching pursuit. Discrete wavelet transform, definition of multi-resolution analysis (MRA). Approximation spaces, scaling function, and the dilation equation, detail spaces. Mother wavelet and the wavelet equation. A view from the frequency domain. Orthogonal wavelets, Daubechies wavelets, Daubechies' least asymmetric filters, coiflets, biorthogonal wavelets. Local trigonometric bases and transforms - discrete sine and cosine transforms. Wavelet packet transform (WPT) and local sine and cosine packet transform. Shift-invariant wavelet transform (MODWT) and WPT's algorithms for pattern recognition. Image segmentation, signal detection and edge identification in seismic signals and images. Wavelet threshold and noise reduction, the minimum squared error threshold. General cross validation (GCV) methods and their applicability for seismic signals. The Bayesian approach or denoising signals and images. Wavelet packet and best basis methods for compression of seismic signals. Algorithms and methods for identification and clustering methods in automated identification of seismic phases, phase and group delay, polarization analysis, locally earthquakes effects.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Climate changes theory from pre-Cambrian to Pleistocene epoch. Climate changes in Holocene epoch – paleoclimatological reconstruction. Climate changes and climate variability in last millennium. Physical and other causes of climate changes in past and in present. Anthropogenic caused climate change. Climate system modeling. Climate change scenarios in 21st century. Possible consequences of climate change – historical analysis and model access to assessment of consequences in the future. Schema approximately covers all contemporary extent of this object. Selection from these topics will be made by adviser according to dizertation thesis theme.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Assimilation cycle of NWP models. Model requirements for atmosphere simulation. Hydrodynamic, Navier-Stokes equations, their fields of application, simplification and their solvability. Numerical solution and its comparison with analytical solution, physical parameterizations of atmospheric phenomena and their influence to the subgrid processes. Postprocessing and its utilization in practice (all kind of traffic, agriculture, social life…).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basics of parallel programming in C/C++
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Basics of parallel programming in Fortran: shared and distributed memory.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Parallel code benchmarking, debugging and optimization.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Basics of graphics accelerator parallel programming for HPC applications.
Paid training activity for participants: No, it's free of charge.
Courses (including on demand) about effective deployment of HPC software such as NWChem, Qwalk.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Working with a supercomputer/computing cluster: from user registration to job submission.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Basics of statistical analysis using neural networks and tensor flow; HPC decision support systems.
Paid training activity for participants: No, it's free of charge
Common methods of handling and statistically analyzing very large amounts of data.
Paid training activity for participants: No, it's free of charge.
Basics of Fortran 90/95 programming with a focus on standard mathematical problems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Basics of working with Linux based operating systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Basics of object oriented programming in Python programming language.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Basics of using R software environment for statistical analysis with a focus on HPC applications.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Using of HPC quantum chemical programs for simulation of molecular properties.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Using of HPC programs for simulations and optimization of chemical processes.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Quantum information processing, tools and usage, existing quantum technologies (IBM, Toshiba, Google, Microsoft,D-wave, Qusoft, idQuantique), Chineese quantum network, quantum satellites, Bells inequality, quantum teleportation, quantum cyphers.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Algorithm, computational complexity, recursion,graphs, matrix operations, search, sort.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basics of artificial inteligence, fuzzy logics, neural networks, evolutionary and genetic algorithms, expert and multiagent systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Problem, algorithm, program, C and C++ programming languages, object oriented programming, memory, files, recursion, source code analysis
Computation stability and precission, comparison of analytical and numerical methods, basic numerical algorithms, optimalisation, chaos, fractals, nonlinear pendulum, nonlinear dynamics, difusion and heat propagation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to modelling: model definition, approaches, classification, hydraulic systems, heat systems, mechanical systems, statistical identification methods, process management.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Divide and conquer algrithms, greedy algorithms, dynamic programming, graph algorithms, priority queues, binary search trees, hashing, data compression.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Artificial intelligence, supervised/unsupervised learning, neurocomputing, neural networks, mutlilevel perceptron, generalization, boosting, AdaBoost, signal analysis.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basic types and uses of autonomous mechatronic systems, sensors, signal integration, motion dynamics, communication and visual systems, artificial intelligence, deep learning.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Mathematical and Physiscal basics of finite element method, finite element classification, fem equation construction and solution methods, ANSYS software modelling of mechatronic systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Bio inspired and evolutionary algorithms, genetic algorithms, evolutionary strategies, agent systems, multi agent systems, robotics and cybernetics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Fundamental algorithms: 1) Search algorithms - linear search, binary search - binary search tree 2) Data structures - priority queue - hash table 3) Graph algorithms - graph properties (components, bipartite) - depth-first search - breadth-first search; dynamic programming -- in C.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Introduction to intelligent data analysis
2. Introduction to data processing in Python
3. Exploratory analysis and data visualization
4. Getting and linking data
5. Exploratory analysis using statistical analysis
6. Data cleaning and preprocessing
7. Preprocessing of textual data
8. Evaluation and model selection
9. Linear and logistic regression
10. Decision trees
11. Numerical optimization and simulations
12. Advanced data analysis topics
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1.Introduction to knowledge discovery and data mining, data characteristics
2.Data preparation a. preprocessing b. transformation
3. Classification a. Decision trees b. Bayessian (Naïve Bayes) c. distance-based d. regression e. neural networks f. support vector machines
4. Clustering a. partitioning algorithms b. hierarchical clustering c. probabilistic clustering d. self-organizing maps and neurla networks
5. Association rules
6. Text and web mining
7. Evaluation of data mining methods
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Introduction to artificial neural networks (NN): inspiration from biology, basic concepts, NN with logic neurons.
2. Binary / continuous perceptron: supervised learning, error functions, learning rules, continuous function gradient, classification.
3. Linear NN: Vector spaces, autoasociative memory. 4
. Multilayer perceptron: supervised learning, errro backpropagation algorithm, model validation, generalization, model selection.
5. Gradient learning methods, introduction to deep learning.
6. Hebbian unsupervised learning, principle component analysis.
7. Semi supervised learning, self-organizing maps, clustering, topographic view.
8. NN with radial base functions (RBF), model training.
9. Hopfield NN model: deterministic dynamics, attractors, autoasociative memory.
10. Sequence data modeling: forward delayed NN, partial and fully recurrent models (RNN), gradient training algorithms.
11. Organization of Status Space in RNN. Networks with echo states (ESN). 12. Stochastic recurrent NN models: Boltzmann machine, DBN model.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Artificial intelligence, its contents, methodology.
2. Problem solving.
3. State space, search for solution.
4. Uninformed search.
5. Heuristic search.
6. Games problem solving.
7. Machine learning.
8. Neural networks and evolutionary algorithms.
9. Mathematical logic for artificial intelligence.
10. Knowledge representation.
11. Uncertain knowledge and its representation.
12. Planning.
13. Multiagent systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Processing quantum information, tools and usage. Existing quantum technologies (IBM, Toshiba, Google, Microsoft, D-wave, Qusoft, idQuantique), China quantum network, quantum satellites. BB84 quantum key distribution, Bell's inequalities, key distribution based on entanglement. Quantum teleportation. Quantum encryption. Quantum bit commitment and quantum coin flipping. Grover algorithm. Quantum processors, set of universal quantum gates, approximation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Flynn taxonomy, Amhdahl's law, Gustafson's law. Shared and distributed memory systems, multiprocessors and multicomputers. Sources of parallelism, instruction level parallelism, data and task parallelism. Parallel program design, communication, synchronisation, data dependence, decomposition, granularity, load balancing. Parallel programming models, threads, message passing interface. Explicit threading - PThreads. Implicit threading - OpenMP. Programming distributed memory systems - MPI. Programming many-core graphic processors - CUDA, OpenCL. Analytical modeling of parallel programs. Patterns for parallel programming.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Introduction to the topic
2. Visual perception, visual representation of data, Gestalt principles, information overload
3. Creating visual representations, visualization reference model, visual mapping, visual analytics
4. Design of visualization techniques, architecture of visualization systems, design patterns for visualization systems
5. Classification of visualization systems 6. Interaction and distortion techniques
7. Visualization of one-, two- and multi-dimensional data, text and text documents
8. Visualization of groups, trees, graphs, clusters, networks, software 9. Metaphorical visualization
10. Visualization of volumetric data, vector fields, processes and simulations
11. Visualization of maps, geographic information, GIS systems 12. Collaborative visualizations
13. Evaluating visualizations.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basic problems and methods in bioinformatics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Methods of quantum chemistry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Quantum chemisry applications.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Introduction to solid state chemistry.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Numerical recipies and programming used in science.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Electron and molecular spectroskopy.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Using quantum chemistry methods and software for master thesis.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
ArcGIS and GRASS software packages.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Commercial and special geophysical software course.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Basic methods and algorythms of Inverse Problems in Geophysics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Computer simulations using quantum chemistry methods.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Modern copmutational methods of quantum chemistry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Relativistic effects in chemistry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Quantum chemistry methods.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Lectures: 1. Biological macromolecules – nucleic acids, proteins, lipids and polysaccharides.
2. Aggregates of biological macromolecules.
3.Cell – its structure and methods of study.
4. Cell – diffusion, osmosis, active and passive transport, channels and pumps, homeostasis.
5. Cell – electric and mechanochemical properties.
6. Tissues and organs.
7. Respiration and blood circulation.
8. Perception – receptors.
9.Biological effects of external physical factors.
10. Complex systems – autocatalytic reactions, catalytic cycles, deterministic chaos.
11. Complex systems - nonequilibrium thermodynamics, formation of organized structures. Seminars: During the first half of semester, eminent Slovak biophysicists from various university and academy institutions will present results of their research. During the
second half of semester, students will deliver their own presentations.
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
1. Phenomenological treatment of transport and fate of drugs in organism.
2. Pharmacokinetic models of drug distribution I.
3. Pharmacokinetic models of drug distribution II
4. Kinetic models of drug action. A
5. Kinetics of drug action on pharmacological target.
6. Physiology-based pharmacokinetics of human body.
7. Physicochemical basis of pharmacokinetics
8. Molecular structure and pharmacokinetic parameters
9. Methods of prediction and optimization of transport properties of compounds.
10. Optimization and interpretation of screening tests.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Subject to the discipline of Pharmacy Informatics as pharmaceuticals and complex structure the data on them. Subject conveniently synthesized Pharmaceutical professional knowledge on pharmaceuticals with the current essential electronic edition collection, treatment and routine use of pharmaceutical data and information.
• Information system as a central concept for Pharmacoinformatics,
• Pharmaceutical computing,
• The computer as an organization of professional pharmacist requirements for the handling of specialized pharmaceutical data and media,
• Current information systems , data banks medicines and drugs,
• Compatibility of pharmaceutical data , their current types and shapes.
• Drugs and medicines, their characteristics in terms of their specificity and informatics to the needs formulated information processes,
• Local and network technologies in the field of medicines and drugs , and work with them ,
• Creating of skills, knowledge and skills to solve theoretical and practical information problems associated with drugs and medicines
• Virtual libraries, bibliographic databases.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Architecture of distributed systems - grid, cloud, massively parallel systems, Introduction to parallel algorithms, Decomposition techniques, Features of tasks and mutual interactions, Mapping techniques and load balancing, Design patterns for parallel architectures, The complexity of parallel algorithms, Performance of parallel algorithms.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Lectures:
1.Introduction to speech recognition.
2.Speech production and its information flow characteristics.
3.Bacis methods – phonetic-acoustic method, template matching, artificial intelligence approach.
4.Digital speech signal processing in MATLAB environment.
5.Short time characteristics in frequency and time domain.
6.–7.Linear predictive coding.
8.Speech detection.
9.–10.Bellman optimality method. Pattern matching. Dynamic time warping algorithm.
11.Speech recognition systems implementation – training and testing.
12.Hidden Markov Models.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Data mining - Knowledge discovery in Database - Clustering - Machine Learning - Classification and Regression - Decision Trees - Ensemble methods - Neural Networks - Application of Machine Learning - R-project - Machine Learning at Python.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Aims of knowledge discovery process, basic concepts and definitions. Applications view. Data processing. Basic technologies and its comparison with learning systems. Data clearing and transformation, attribute tests, error analysis. Decision and fuzzy decision trees. Decision rules and its conversion into decision tree. Rules generation. Searching algorithms. Clustering. Statistical technologies. Visualisation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Visualization of univariate data (density plots, histograms, q-q plots, empirical CDF, box and whisker plots, barplots)
2. Visualization of bivariate data (scatter plots and simple regression)
3. Visualization of multi-dimensional data, heatmaps, contourplots, PCA, LDA.
4. Visualization of time series data, interpolation, smoothing, periodograms, spectrograms, autocorrelation.
5. Linear regression.
6. Modelling of time-series data (ARMA and ARIMA)
7. Generalized linear models.
8. Support vector machines.
9. Convolutional neural networks.
10. Unsupervised models - K-means.
11. Unsupervised models - GMM
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to the theory of fuzzy sets, fuzzy numbers and arithmetical operations, comparison of fuzzy numbers, fuzzy relations, fuzzy logic, fuzzy interference rules, fuzzyfication and defuzzyfication, optimization problems with fuzzy coefficients. Artificial neuron, characteristics of neural networks, multi-layer neural feedback network, training of a neural network, stability of neural network, verification of neural network model, fuzzy neural networks, applications in decision problems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Parallel architectures and parallel programming
2. Characteristics of parallel algorithms and parallel problems
3. Conditions of parallelizability, Flynn's taxonomy, Amdahl’s and Gustafson’s laws
4. Methodology for designing parallel programs – decomposition, communication, synchronization, data dependency
5. Parallel programming models, threading model, message-passing model
6. Threading methods – explicit (POSIX Threads, Java, C++) and implicit (OpenMP)
7. Distributed memory systems - MPI
8. MPI (MPICH) – data types, communicators, barriers, semaphores
9. Managing MPI group communication
10. Analytical modelling of parallel systems, analysis of complexity and performance, complexity classes Polylog and P-complete
11. Parallel programming patterns (sorting, searching, graph algorithms, dived and conquer method)
12. Programming multicore graphics processing units
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
1. Elementary principles, basic terms, issues in Artificial Intelligence (AI)
2. Technical and programming means of AI
3. Programming language PROLOG, syntax, data types
4. Control structures, standard predicates
5. Strategies of problem solving
6. Heuristic search
7. Problem reduction, AND-OR graphs
8. Natural language analysis and understanding
9. Expert systems
10. Pattern recognition
11. Methods of knowledge engineering
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Introduction to data science, statistics and probability, model building, machine learning in MS Azure, ML services on an SQL server, introduction to R programming language.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Advanced machine learning algorithms and techniques using Azure ML and SQL server.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Basics of programming in R programming language, data transfer and analysis.
Microsoft cloud services deployment for big data processing, appropriate system architectures, Azure Stream Analytics, Azure Data Lake Store, Azure SQL Data Warehouse and Factory.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Knowledge of design management of partially known dynamic system with a dynamic, structured and parametric uncertainty model and methods of synthesis of robust controllers. For complex systems with a high degree of uncertainty to apply knowledge-based approaches to qualitative models, fuzzy models and neuro-fuzzy models for designing intelligent control systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Gain knowledge of simulation and optimization of dynamical systems associated with the current scientific work of the student. Ability to formulate and solve the problem of simulation methods and analyze the results obtained. Get an overview of methods, procedures and principles of static and dynamic optimization solutions and show the possibilities of current doctoral student tasks in the software MATLAB - Simulink.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Allow students to obtain postgraduate knowledge of methods and solutions of design optimization of piston combustion engines. Formulate complicated problems of the engine loads by operation, analyze and provide solutions of design optimization problems with the application of modern computational methods and simulation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge
Using of HPC civil engineering programs for simulation in construction of buildings
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
OOP programming in geodesy.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Data mining in construction of buildings.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Creation of HPC civil engineering programs for simulation in construction of buildings.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Using of HPC for numerical simulation in structural mechanics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Basic course of artificial intelligence
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts
Basic course of machine learning
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler) Machine/Deep Learning concepts
Introduction to the issue of hybrid means of computational intelligence.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Advanced course of machine learning.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler); Machine/Deep Learning concepts
Course on evolutionary algorithms.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Basic cloud characteristics, cloud computing models, virtualization and hypervisor.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Models and architecture of information and control systems (IRS) for small, medium and large enterprises with emphasis on the level of MES (Manufacturing Execution System) and CPPSS (Collaboration Planning and Production System and Services).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler)
Practical skills in applying advanced methods of data analysis focused mainly on multidimensional data.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts; Domain-specific background knowledge
Deep learning using neural networks and convolutional neural networks.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts; Domain-specific background knowledge
Basic course of bioinformatics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Parallel computer architectures, starting from their computational models, through the principles of parallel execution of the computational process to architectural concepts of parallel systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: HPC tools (e.g. profiler, scheduler); MPI; OpenMP; Domain-specific background knowledge
Basic course of artificial intelligence.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts
Structure of software systems based on numerical methods of mechanics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Theoretical and practical knowledge in the field of advanced mathematical, usually numerical methods for solving problems and simulations of aerospace systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Basic information about high-performance computation applied mathematics on supercomputers.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Calculations of deformation resistances and forming forces. Use of CAD / CAM systems for the construction of tools and forgings. Fundamentals of technology in relation to the creation of the structure and properties of materials.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Optimization methods and tasks, optimization model of thermal process, optimal parameters of operation. Use of modeling of thermal processes using CFD software.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Advanced methods of linear programming. Advanced methods for linear optimization. Creation of mathematical models of technological processes.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Aggregation and standardization of resources into a data warehouse / database suitable for processing the problem.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Learning outcomes of the course unit:
Within the study of Applied Mechanics, students obtain knowledge about the properties and behavior of discrete and continuous mechanical systems. They will get an overview of the method and the methodology of computational models to determine the stress of mechanical systems. They learn to solve problems of vibrations of continuous and discrete mechanical systems and to explore the possibilities for elimination undesired effects resulting from vibration. They will receive knowledge of the theoretical foundations and practical applications of numerical computational methods in mechanics.
Course contents:
•Principle of mathematical modelling of mechanical systems.
•Methods of analysis of mechanical systems.
•Vibration of discrete mechanical systems-SDOF, MDOF.
•Fundamentals of continuum mechanics.
•Vibration of continuum systems.
•Numerical methods in mechanics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
The goal is to teach basic knowledge about data acquisition methods and data acquisition devices. The student will gain practical skills with selected data acquisition devices.
Course contents:
Measurement process description
Measurement theory
Measurement system description
Sensors
Measurement equipment
Software
Neural networks - Hebb's learning, Back-Propagation
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts; Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
The goal of the subject is to obtain knowledge and skills in modern methods of modeling, control and optimization by using knowledge engineering, fuzzy systems, genetic algorithms and neural networks.
Course contents:
1. Introduction. Relationship between ICM and control theory.
2. Artificial intelligence.
3. Problems solution by inteligent methods.
4. General problems solvers (GPS, STRIPS, ...).
5. Expert systems.
6. Knowledge engineering.
7. Uncertainty in decision making.
8. Fuzzy systems and fuzzy control.
9. Knowledge-based control.
10. Genetic algorithms.
11. GA in control.
12. Neural networks. Basics. Learning. Deep learning.
13. NN applications in control.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts Numerical methods (linear algebra, statistics)
Learning outcomes of the course unit:
To teach building of discrete simulation model. The main method is discrete-event simulation. The goal is oriented on the design simulation models of production systems and services. Student obtains practical experiences with simulator Witness.
Course contents:
Principles of simulation (key terms)
Basic simulation concepts
Random variable, standard distribution.
Introduction to queuing theory- queuing systems M/M/1, M/M/n,
Queuing Networks
Input, output rules in Witness
General procedure of model building. Problems of verification and validation.
Simulation optimization
Simulation languages and simulators.
Methodology of software selection.
Principles of continuous simulation.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
Subject "Modeling and simulation of technological processes" allows students to enter into the problem of development and solution of simulation models. The basis of computer simulation is the finite element method. Students will obtain basic theoretical knowledge and practical skills in modeling and numerical simulation of chosen technological processes. Emphasis is placed on understanding of the basic philosophy of design, verification and application of simulation models for solving engineering problems as a means for progressive design, analysis and optimization of technological processes. Students will also acquire an overview of the commercial specialized software systems for simulation of technological processes.
Course contents:
Theoretical basis for modeling, models of processes and systems, distribution patterns according to process parameters, the selection of a method and model solutions. Theoretical basis for modeling of temperature, stress-strain and electro-magnetic fields. Characteristics of the basic material models - elastic, elasto-plastic, visco-plastic, hyperelastic. Material Databases. The finite element method. Overview of commercial software systems for simulation of technological processes. Development of simulation model. Development of geometric models. Generation of finite element mesh. Definition of linear and nonlinear material properties, modeling of phase transformations, input of initial and boundary conditions. Specification of computation methods. The accuracy and stability of the solution. Processing and evaluation of the results of numerical solutions. Numerical simulation of selected technological processes depending on the major specification- Numerical simulation of heating pocesses and processes including the energetical influence of surfaces, heat treatment processes, forming, welding, casting using the ANSYS software or other available software systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
Subject Modelling of Mechatronic Systems is focused on the problems of modeling and analysis
of mechatronic systems. The main focus is to give students a knowledge of the methods and
procedures to create dynamic models of mechatronic systems (to generate: equations of motion
of mechanical and hydraulic systems, electrical circuit equations) and their solutions.
They will be able to use the results obtained from the solution of models for analyzing the
response and behavior of the model (i.e. the actual system). Students will be able to apply
their knowledge in solving complex tasks related to multiphysical character of mechatronic
systems.
Course contents:
•Structural analysis of mechatronic systems.
•Mathematical models of mechatronic systems.
•Methods for formulation: equations of motion of mechanical systems, equations of motion for fluid systems, electrical circuits equation.
•Modelling and analysis of mechatronic systems.
•The classification of processes in mechanical, fluid and electrical systems.
•Computer tools (ANSYS, Matlab-Simulink, MSC-Adams, Dynast) and their application for modeling and analysis of mechatronic systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
Student will enhance his/her knowledge of computational thermodynamics in the field of materials science, he/she will be able to apply CALPHAD method in computations of phase equilibria and use the software Thermo-Calc and Dictra.
Course contents:
1) Introduction, microstructure of metallic and non-metallic materials
2) Definitions in thermodynamics of materials
3) Thermodynamic description of one-component system
4) Thermodynamic description of solutions, mixtures and intermediate phases
5) Thermodynamics of interfaces and material processes
6) Proposal and assessment of thermodynamic databases
7) Case studies.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
Students will obtain basic knowledge in modeling and numerical simulation of processes of heat and mass transfer using the finite element method (FEM). The emphasis is placed on the application of advanced computational methods, mathematical modeling and numerical simulation of temperature fields in chosen technological processes using the program code ANSYS.
Course contents:
1. Modeling and simulation. Models of processes and systems, classification of models according to process parameters, the selection of a method and model solutions. How to create simulation model. Overview of software systems for simulation of thermal processes. The program code ANSYS. Preprocessing, solver, post-processing.
2. Subject and methods of thermodynamics, state variables, the thermodynamic system. Basic laws of ideal gas. The first and the second law of thermodynamics.
3. Thermophysical properties of solids. Material databases. Experimental methods for the measurement of thermophysical properties. Basic terms and laws of heat transfer. The temperature field, heat flux, heat flow. Heat conduction, convection, radiation.
4. Heat diffusion equation, Fourier-Kirchhoff's differential equation of heat conduction. Geometric, physical, initial and boundary conditions. One-dimensional steady-state heat conduction in the bodies without and with internal heat sources.
5. Transient heat conduction in an infinite slab and in an infinite long cylinder without and with internal heat sources. Heat conduction in bodies with finite dimensions. Transient heat conduction in a semi-infinite region.
6. Convection heat transfer, the basic concepts. Newton's law. Mass, momentum and energy conservation. Boundary layers. Theory of similarity, dimensional analysis. External free convection, empirical correlations. Free convection in enclosures.
7. External forced convection. Internal forced convection in pipes and channels. Heat transfer during phase transformations. Radiation heat transfer. Basic principles and terms. Planck distribution, Wien's laws, Stefan-Boltzmann law, Kirchhoff's law, Lambert's law. The methods for calculation of radiation heat transfer. Combined heat transfer by convection and radiation.
8. Analytical and numerical methods for solution of heat diffusion equation. Solution of thermal problems applying finite element method. Linear and nonlinear problems. Space and time discretization. Accuracy and stability of numerical solutions.
9. Solution of coupled heat and stress-strain problems. Numerical simulation of material heating in industrial furnaces. Modeling of material cooling during hardening.
10. Numerical simulation of the processes of fusion welding. Simplified models of welding processes. Modeling of the heat input to the weld.
11. Solution of coupled electro-magnetic and thermal problems. Resistive electric heating, induction heating, laser heating.
12. Development of simulation models for the flow of incompressible and compressible fluids. Modeling of the cavity filling during the casting processes.
13. Modeling and numerical simulation of the heat transfer in technological processes using specialized software.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
Learning to program in object-oriented development environment for the example of Microsoft Visual Studio and SharpDevelop. Devoted to the practice of object-oriented programs, principles and rules-driven programming events. Coping with standard ways of creating user components, application development and design of their user interface.
Course contents:
- .NET Framework, C# and .NET Framework, comparison of C++ and C#.
- Common Language Infrastructure, Memory Management, Common Type System.
- Data Types in C#. Variables and constants.
- Management structure. Cycles and branching in C#.
- Arrays in C#. Working with files.
- Object-oriented programming in C#. Base class.
- Inheritance, overloading methods, abstract methods. Modifiers access, virtual and abstract properties.
- Class System. Object, assemblies, namespaces.
- Treatment of errors, exceptions, handling exceptions.
- Directives, interfaces, events.
- Graphical User Interface, Windows Applications.
- Windows Forms. Basic components - Button, Label, TextBox, ComboBox.
- Access to essential databases. Basic components database DataSet, DataGriD.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
Learning outcomes of the course unit:
Presenting of Computer Integrated Manufacturing as a philosophy concept. Teaching of the structure and function of individual subsystems Computer Integrated Manufacturing, presenting of modern approaches of building Computer Integrated Manufacturing. Solving of integration problems of individual subsystems.
Course contents:
- Computer Integrated Manufacturing
- Hierarchical levels of information systems
- CIM - standardized business model
- CIM components
- ERP systems - the core of enterprise information system
- Data Warehousing
- Data Warehousing - Multidimensional modeling
- Data Mining
- Basic principles of integration in CIM
- The basics of integration of business systems
- Service Oriented Architecture - SOA
- New trends in industry - Industry 4.0
- Storing, processing and analyzing of large volumes of structured and unstructured data.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts; Numerical methods (linear algebra, statistics); Domain-specific background knowledge.
Learning outcomes of the course unit:
The student will acquire basic knowledge about principles, methods and algorithms of 2D computer graphics and digital image processing. He will know how graphics works on a computer and will be able to use the functions of graphical application programming interfaces and libraries of Windows operating system in connection with selected application model as well as dotNET technology. Can use graphical application programming interface and program simple 2D graphics applications in C # (C ++) using Visual Studio integrated development environment.
The student will acquire basic knowledge about digital representation of image and its processing. He will learn about the possibilities of image processing in MATLAB and get acquainted with the basics of its programming.
Course contents:
1. Programming technologies and computer graphics - summary of knowledge (allowance 2/2)
2. Introduction to computer graphics (allowance 4/2)
3. Graphics programming in Windows (allowance 4/4)
4. Geometric transformations (allowance 2/2)
5. Viewing transformations (allowance 2/2)
6. Rasterization of basic geometric objects and filling of regions (allowance 2/2)
7. Colours in computer graphics (allowance 2/2)
8. Basics of digital image processing (allowance 6/6)
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++; Domain-specific background knowledge.
Learning outcomes of the course unit:
The student will learn the basics of algorithm and procedural programming. Can convert a given task into an abstract space, create a solution algorithm, and program it in C ++ language. He is able to divide the solution into functional units. Has knowledge of selected sorting and search algorithms. He can use basic STL containers. Has the knowledge about use of pointers and dynamic allocation and release of memory. Understands the reason for adhering to the rules of writing a clear and comprehensible source code.
Course contents:
1. Algorithm, program, history of C++, the first program.
2. Program structure, simple functions, data types, variables and constants.
3. Expressions and commands, operators, branching and cycles.
4. Function, declaration, definition, value parameter passing, recursion.
5. Arrays, strings, pointers and references, parameter passing by pointer and reference.
6. Object-oriented programming basics.
7. Pointers and references in the OOP, new and delete.
8. Working with streams, details of cout and cin, working with files.
9. Detail on methods and overloading.
10. Inheritance.
11. Dynamic data structures - linear list (queue, stack, list), tree.
12. Modular programming, preprocessor directives.
13. Namespace, exceptions and error treatment.
14. Programmer's culture, maintainable source code, common errors.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++; Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Learning outcomes of the course unit:
The students will gain knowledge about the basic characteristics of cognitive sciences and their methods. They are able to analyse the essence of interdisciplinary research of cognitive processes and current paradigms of scientific explanation of cognition.
Course contents:
Definition of the term cognitive science. Cognitive linguistics, computational science, and cognitive neurology. The research of the brain damage. The psychophysical problem (relation between mental and physical). The cognition and language: the structure of language, relation of language, and thinking. Computing science, learnability, explainability,Turing's machine, artifical intelligence, robotics.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Machine/Deep Learning concepts; Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Main objectives of the course:
The aim of the course is to provide a set of knowledge in the field of information technology and business information systems, computer and programming systems to support management and decision-making at individual levels of the company.
Brief scheme of the course:
Content of lectures:
• Information System.
• Databases.
• Collection of information.
• Data warehouses.
• Data Mining.
• Power Management Systems documents.
• Computer networks.
• Internet services and tools.
• E-business.
• E-commerce.
• Business information systems.
• TPV systems.
• CAX technology.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Basic principles of programming with sending messages. Basic types of
messages between two processes in MPI. Blocking and non-blocking operations in MPI. Group
communication. Communicators.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
History of grid computing. Theoretical foundations of grid computing. Computer architectures connected to the grid computing. Creation of a computer cluster. Middleware and its
functionality. Computer grid security. Virtualization tools. Theoretical foundations of
computer clouds. Hardware and software solutions for providing cloud services. Model
cloud services. Creating a private cloud.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Objectives and properties of parallel calculations. Classification of resources for parallel and distributed computations.
Decomposition of parallel problems. Definition of acceleration and efficiency of parallel calculation.
Total acceleration of the calculation. Amdahl's rule. Ways and means of using massive and
expansive parallelism in the program model of data parallelism. Characteristics of the model
SPMD and MPMD. Basic types of operations for passing messages between two processes.
Blocking and non-blocking operations.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Introduction to AI. State space. Methods of artificial
intelligence (UI), cognitivity, knowledge and intellect, areas of UI research. Heuristic search
solutions. Use of mathematical logic in UI, basic elements of predicate logic, situational
logic. Planning and production systems, framework systems, multi - carrier systems, artificial intelligence and computability. Turing machines, stopping problem. Introduction to machine learning, basic concepts, decision tree creation, learning with a teacher, learning without a teacher.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Classification of models. Discreet dynamic systems - representation of linear, time invariant (LTI) discrete systems using differential equations, z-transform, transfer function and LTI stability criterion
discrete systems. Modeling of continuous systems and processes - modeling of nonlinear ones
differential equations using integrators, frequency characteristics of continuous LTI systems.
Serial and parallel arrangement of subsystems. Negative and positive feedback,
stabilization of systems by means of feedbacks, control technology.
Software packages for scientific and technical calculations and simulation MATLAB and SIMULINK.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Introduction to the theory of neural networks. Learning process in neural networks
with and without teacher. Neural network as universal approximator. Practical experience with neural networks, classification models, prediction models. Decomposition of a set of objects into training and testing set. Optimal descriptor selection, neural network architecture and number of learning steps. Selected applications of neural networks.
Introduction to multidimensional data analysis. Visualization of multidimensional data. Selected
supervised models, non-preserved models. Introduction to big data analysis. Visualization of big data. Selected algorithms for large scale analysis. Software and hardware specifics of big data analysis.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Virtual organizations and conditions of membership in them. Computer grid security. Slovak
grid initiative. JDL language and program gridification. Middleware gLite and its commands. Sequential programs in a computer grid. Distributed computations in a computer grid. Parallel programs in a computer grid. Applications using cloud services.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
The role of algorithms in computation. Recurrence. Analysis of time complexity of sorting and searching algorithms.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Introduction to concepts in the field of image analysis and processing. Image representation.
Image preprocessing. Morphological operations. Fourier transform. Symptoms. Detection a
description of symptoms. Segmentation. OpenCV library.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Objectives, structure and supporting elements of grid, cloud and HPC computing. Basic models
management of grid, cloud and high-performance computing systems. The concept
elastic cluster. Scheduling and classification of scheduling. Defining a set of machines,
sets of tasks and optimization criteria. Scheduling tasks in grids and clouds from a perspective
high performance computing.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Introduction to R. R for Windows, Rstudio.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
https://vsc.ac.at/training/2021/VSC-Linux-Mar/
This Linux command-line course is for users (or soon to be users) of the VSC clusters only. You will learn how to login to VSC and step-by-step we will show you how to work on the Linux command line and a few basic things that will help you to organise your workflows on the cluster. Focusing on hands-on teaching throughout the course, you will immediately try out what you've heard and adapt it to your own needs.
After attending this course you are prepared for and might consider to continue with the course Introduction to Working on the VSC Clusters to be able to use the VSC supercomputers and especially their queuing system efficiently.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
https://vsc.ac.at/training/courses/VSC-Intro
In this course we will help you getting started on the VSC clusters, Austria's most powerful supercomputers. With running and developing software on a supercomputer there are many similarities and fewer but crucial differences compared to your desktop PC. Focusing on hands-on teaching throughout the course, you will immediately try out what you've heard and adapt it to your own needs.
This lecture is equally relevant to those who will merely be running existing software as to those who will develop scientific codes.
This course is from beginners level (the first steps on a supercomputer) to intermediate level (some experience on VSC or an other compute cluster).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Linux command line
https://vsc.ac.at/training/2021/C++/
This advanced C++ training is a course on software design with the C++ programming language. The focus of the training are the essential C++ software development principles, concepts, idioms, and best practices, which enable programmers to create professional, high-quality code. Additionally, the course gives insight into kernel development with C++. The course provides insight into different design strategies (object-oriented programming, functional programming, generic programming) and the philosophy of “Modern C++” and teaches guidelines to develop mature, robust, maintainable, and efficient C++ code.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
https://vsc.ac.at/training/courses/NLPE
This course covers performance engineering approaches on the compute node level. Even application developers who are fluent in OpenMP and MPI often lack a good grasp of how much performance could at best be achieved by their code. This is because parallelism takes us only half the way to good performance. Even worse, slow serial code tends to scale very well, hiding the fact that resources are wasted. This course conveys the required knowledge to develop a thorough understanding of the interactions between software and hardware. This process must start at the core, socket, and node level, where the code gets executed that does the actual computational work. We introduce the basic architectural features and bottlenecks of modern processors and compute nodes. Pipelining, SIMD, superscalarity, caches, memory interfaces, ccNUMA, etc., are covered. A cornerstone of node-level performance analysis is the Roofline model, which is introduced in due detail and applied to various examples from computational science. We also show how simple software tools can be used to acquire knowledge about the system, run code in a reproducible way, and validate hypotheses about resource consumption. Finally, once the architectural requirements of a code are understood and correlated with performance measurements, the potential benefit of code changes can often be predicted, replacing hope-for-the-best optimizations by a scientific process.
This course provides –via lectures, demos, and hands-on labs– scientific training in Computational Science, and in addition, the scientific exchange of the participants among themselves.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: OpenMP; C/C++
OR Fortran.
https://vsc.ac.at/training/2020/OpenMP-Nov/ TENTATIVE (link is to the previous edition)
The focus of this 2 days course is on shared memory parallelization with OpenMP for dual-core, multi-core, shared memory, and ccNUMA platforms. This course teaches OpenMP starting from a beginners level. Hands-on sessions (in C and Fortran) will allow users to immediately test and understand the OpenMP directives, environment variables, and library routines. Race-condition debugging tools are also presented.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
https://vsc.ac.at/training/2020/MPI-Nov/–> TENTATIVE (link is to the previous edition)
On clusters and distributed memory architectures, parallel programming with the Message Passing Interface (MPI) is the dominating programming model. This 4 half-days course teaches parallel programming with MPI starting from a beginners level. Hands-on sessions (in C and Fortran) will allow users to immediately test and understand the basic constructs of the Message Passing Interface (MPI).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++ OR Fortran
https://events.prace-ri.eu/event/1009/ –> TENTATIVE (link is to the previous edition)
Most HPC systems are clusters of shared memory nodes. To use such systems efficiently both memory consumption and communication time has to be optimized. Therefore, hybrid programming may combine the distributed memory parallelization on the node interconnect (e.g., with MPI) with the shared memory parallelization inside of each node (e.g., with OpenMP or MPI-3.0 shared memory). This course analyzes the strengths and weaknesses of several parallel programming models on clusters of SMP nodes. Multi-socket-multi-core systems in highly parallel environments are given special consideration. MPI-3.0 has introduced a new shared memory programming interface, which can be combined with inter-node MPI communication. It can be used for direct neighbor accesses similar to OpenMP or for direct halo copies, and enables new hybrid programming models. These models are compared with various hybrid MPI+OpenMP approaches and pure MPI. Numerous case studies and micro-benchmarks demonstrate the performance-related aspects of hybrid programming.
Hands-on sessions are included on both days. Tools for hybrid programming such as thread/process placement support and performance analysis are presented in a "how-to" section. This course provides scientific training in Computational Science and, in addition, the scientific exchange of the participants among themselves.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
This two half-days course will be about parallel I/O with a special focus on portable data formats. It will introduce the use of the HDF5 and NetCDF (NetCDF4 and PnetCDF) library interfaces, and hands-on exercises (in C/C++ or Fortran) will allow to immediately test and understand their usage. Performance hints, optimization potential, and best practices for I/O will be discussed in detail throughout the whole course.
Numerical simulations conducted on current HPC systems face an ever growing need for scalability pushing the limitations on size and properties that can be accurately simulated. Therefore, ever larger data sets have to be processed, be it reading input data or writing results. Serial approaches on handling I/O in a parallel application will dominate the performance on massively parallel systems, leaving a lot of computing resources idle during those serial I/O phases.
In addition to the need for parallel I/O, input and output data is often processed on different and maybe even heterogeneous platforms. Conversion processes can impose a high level of maintenance when different data representations are needed. Portable, self-describing data formats such as HDF5 and netCDF can help to solve these problems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++ OR Fortran
This course brings together the two domains of Big Data and High Performance Computing (HPC) by showing how to run Hadoop jobs on the Vienna Scientific Cluster (VSC). High Performance Computing applications are usually highly optimized to make efficient use of the available processing power of compute clusters called supercomputers, especially at the level of floating point operations. Big Data applications operate at a higher level on large data sets and their main focus is on features such as fault tolerance, processing of dirty and/or unstructured data, and fast development. The largest Big Data clusters are even larger than supercomputers and require programming paradigms with even better scaling behavior than it is required in HPC. Tools to facilitate Big Data processing include the MapReduce framework and its more modern Spark counterpart, as well as SQL and NoSQL databases.
The course contains a quick overview of the VSC environment, of the module environment and of the involved schedulers. Scheduling becomes an issue when combining Big Data and HPC: we use the Slurm scheduler to gain access to compute nodes for a job and within the job we spawn a Big Data scheduler (mostly Yarn) which schedules and starts user tasks.
This course provides lectures, demos, and hands-on labs. The hands-on labs will be done on our flagship system VSC-4, all participants will get a temporary training user account for the course.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran OR ) Python
Learn how to accelerate your applications with OpenACC, how to train and deploy a neural network to solve real-world problems, and how to effectively parallelize training of deep neural networks on Multi-GPUs.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran OR ) Python
MODULE 1: INTRODUCTION TO AI
Definition and delimitation of terms
History of Artificial Intelligence
Examples of use: Potentials and Red Flags
Ethical issues in the application of Artificial Intelligence
State-of-the-art AI methods e.g. classification, clustering, regression, etc.
Takeaway: Basic knowledge on the topic and on currently emerging innovations as well as an overview of relevant methods. Participants will be able to identify which approaches are relevant for use cases and suitable for solving problems or fulfilling requirements.
MODULE 2: PRACTICE DAY
Presentation of exemplary positive and negative examples
Hands-on development of AI application scenarios
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: No prerequisite knowledge
MODULE 1: BIG DATA INTRODUCTION
Big Data Basics: Definitions, Trends
Data, information, knowledge: data types, data origin, dark data
Data-driven business models, use cases, success stories
Legal aspects: Data Ownership, Data Protection, Copyright, Contract design, Trade secrets
Takeaway: Basic knowledge of the topic and current business model innovations. Participants are subsequently able to initiate data-driven innovation projects in their own company.
MODULE 2: DATA SCIENCE Basics of statistics: terms, definitions, basic concepts
Data acquisition: Batch vs. Stream, Micro-batching, CAP
Data pre-processing and integration: ETL, Messaging queues, Outliers, Missing values
Data analysis: Machine Learning: Supervised & Unsupervised, Regression, Classification, Clustering, Bias
Data visualization: possibilities and variants
Takeaway: Overview in the field of data science and knowledge of relevant methods. Participants are able to decide in individual cases which practices are relevant for use cases and suitable for solving the problem or fulfilling the requirement.
MODULE 3: BIG DATA TECHNOLOGIES Basic technologies: Data Management Platform Lifecycle
Apache Hadoop Ecosystem: Hadoop & Ecosystem, HDFS, MapReduce, YARN
Apache Spark: Framework, Architecture, Libraries
NoSQL: Concepts, Column, Key-Value, Document, Graph
Tools and Suites: Open Source vs. Commercial, Enterprise Ready Tools, Cloud vs. On Premise
Takeaway: Knowledge of the current technology ecosystem. Ability to select suitable technologies and tools to solve the problem or to best meet the requirements.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: No prerequisite knowledge
Together with experts from Know-Center, we will introduce you to the Data Service maps in this 4-hour workshop. Following other successful methods such as the well-known St. Gallen Business Model Canvas, we work with the Data Service Cards and the Data Product Canvas of Know-Center in small groups to develop your new data services.
Type of methodology: Hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: No prerequisite knowledge
At our Summer Academy you will have an interactive first-hand experience of the latest research results from the key areas of Big Data and Artificial Intelligence. Join us on a journey and explore topics ranging from Federated Machine Learning, Quantum Machine Learning, Privacy-Preserving Analytics and Explainable AI to Immersive Analytics Environments and Guided Analytics, as well as state-of-the-art work environments that redefine the division of labor between man and machine. Let us explore the potential of these developments for your business together! AGENDA: Resilient supply chains for regional value generation. Immersive Computing for the Digital Industry. Hybrid Modelling and Theory-Driven Data Science. Deep Reinforcement Learning. Time Series Analytics. Human Aware Data Analysis - Visual & Guided Data Analytics. Data Driven Business Models - How to turn Data into Business. Digital Transformation at Work. Recommender Systems. Data Science (in the Real World). Explainable AI. Privacy-Presering Analytics and Quantum Computing. Natural Language Processing, Digital Media Analytics & Conversational Agents. Data Analytics and AI in Life Sciences and Pharmaceutical Industry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: No prerequisite knowledge
This training introduces basics of Deep Learning and how to tackle problems such as chum prediction and image classification. AGENDA: How do neuronal networks learn. Feed-Forward Networks & Mathematics. PyTorch basics. Data processing in PyTorch. Train and Analyse Feed-Forward networks . Understanding problems in neuronal networks. PyTorch to PyTorch Lightning. Table-based modelling with Neuronal Networks.
Type of methodology: Lecture
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: No prerequisite knowledge
Digitization is becoming more and more important. This course offers an overview about trends in advanced data analytics. AGENDA: Introduction to Big Data and Advanced Data Analytics. Digitization: New Digital Data. Big Data for SMEs. Use Cases. Dos and Donts in Data Business. Business and Platform Ideas. Generation of digital Product- and Sericeideas.
Type of methodology: Lecture
Participants receive the certificate of attendance: Yes
Paid training activity for participants: Yes, for all
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledgeHPC tools (e.g. profiler, scheduler)
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Python
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++ OR Fortran
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Python
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Intensive course; lectures and personal training, application for specific cases.
Access to the HPC cluster http://physon.phys.uni-sofia.bg/about-physon-en
Type of methodology: Combination of lecture and hands-on; Self learn
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge:
C/C++; Fortran; Python; HPC tools (e.g. profiler, scheduler); MPI; OpenMP; Machine/Deep Learning concepts; Numerical methods (linear algebra, statistics); Domain-specific
Language: English and Bulgarian
Lectures, personal training, personal projects.
The aim is to develop a running application for specific cases
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++; Fortran; Python; HPC tools (e.g. profiler, scheduler); MPI; OpenMP; Numerical methods (linear algebra, statistics); Domain-specific background knowledge
Language: English and Bulgarian
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics)
Language: Bulgarian
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics)
Language: Bulgarian
The forthcoming training is intended for members of the teams working on the NPP “Environmental Protection and Reduction of the Risk of Adverse Events and Disasters” and on the scientific infrastructure “National Geoinformation Center “. The purpose of the training is to engage new users (scientists, students, PhD students) wishing to use computing resources of the supercomputer Avitohol in the implementation of their activities. The training will be conducted remotely, using the Zoom platform, and information about the event will be sent to the emails of registered participants.
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Language: Bulgarian
The course aims to familiarize participants with the various methods and approaches for performing distributed computations using the infrastructures available in Bulgaria and Europe, such as Grid and the Avitohol supercomputer, as well as servers equipped with powerful graphics cards. The options for access, performing computational tasks, and data storage will be considered. Various methods for distributing the computations will be tested when multiple servers are used simultaneously. It will be possible to use either codes developed by the participants or popular open-source applications.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Language: Bulgarian
The course aims to acquaint the PhD students with parallel architectures and algorithms. It also includes practical exercises using standard parallel programming MPI and OpenMP. The course covers:
Parallel architectures, parallel algorithms and evaluation of parallel efficiency;
Standards for parallel programming;
Basic operations using the standard MPI and OpenMP;
Methods and paradigms for parallel programming.
The course is suitable for PhD students with an interest in programming and/or the use of modern HPC systems, as the supercomputer Avitohol and the newest HPC system in IICT-BAS.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Domain-specific background knowledge
Language: Bulgarian
Learn how to obtain access to the infrastructure and submit jobs.
Discuss specific needs of their research tasks and how their efficient parallelization.
Type of methodology: Demonstration and discussion
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Language: Bulgarian
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all
Language: Bulgarian
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: If requested
Paid training activity for participants: Yes, for all
Language: Bulgarian
The main goal of the „Business Intelligence and Data Mining“ course is to acquaint the students with the Business Intelligence (BI) concept and the architecture of BI systems as an environment for timely provision of quality information to support the decision management process. The course will also introduce to the students the data mining concept and the main steps in the process of knowledge discovery in databases. The main types of data mining tasks will be discussed, including data exploration, clustering, association analysis, prediction, outlier detection. A special focus will be put on the approaches and methods used for text mining, as well as the implementation of text mining for sentiment analysis. During the classes, the students will use specialized BI software to develop BI applications, and Data Mining tools to analyze data by implementing different data mining techniques, thus learning how to extract managerial information and knowledge from available business data.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all
Language: Bulgarian
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all
Language: Bulgarian
Learning Outcomes
- Define the basic concepts of artificial intelligence
- Distinguish between symbolic and connectivistic approaches to AI
- Apply state search algorithms and biologically inspired optimization algorithms on basic problems.
- Solve basic problems using logic programming
- Apply inference algorithms on basic logical problems
- Compare among various approaches to representing uncertainty
- Assess the applicability of different AI methods on a given AI problem
- Apply the basic machine learning algorithms
- Review the philosophical aspects of artificial intelligence
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
The course "Pattern Recognition” enables the students to understand basic, as well as advanced techniques of pattern classification and analysis that are used in machine interpretation of a world and environment in which machine works. Pattern recognition is basic building block of understanding human-machine interaction.
Learning Outcomes
- Explain and define concepts of pattern recognition
- Explain and distinguish porocedures, methods and algorithms related to pattern recognition
- Apply methods from the pattern recognition for new complex applications
- Analyze and breakdown problem related to the complex pattern recognition system
- Design and develop a pattern recognition system for the specific application
- Evaluate quality of solution of the pattern recognition system
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
Students will have good understanding of existing parallel computer and parallel programming models. Students will be able to design and implement a parallel algorithm maintaining the desired quality properties. Students will have practical knowledge of basic programming tools for parallel program design and quantitative performance analysis.
Learning Outcomes
- Describe parallel computation and parallel programming models
- Describe PRAM computer model
- Apply PRAM programming model in parallel programming
- Apply MPI technology in parallel program development
- Recognize phases of parallel algorithm design
- Combine parallel algorithm development elements
- Evaluate efficiency and scalability of parallel algorithms
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
Students master the main principles of mobile robotics and gain basic knowledge for design and development of mobile robots control and navigation systems.
Learning Outcomes
- Alasify mobile robots according to various criteria
- Analyze driving mehanisms and sensor system sutable for intended application
- Assembly sensors and actuators with the embedded computer system on mobile robot
- Develop sensor fusion algorithms
- Develop motion planning algorithms
- Develop motion of mobile robots localization
- Develop algorithms of environment 2D map building
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
After completing this course students will be able to analyse and design learning automata based solutions, and to select suitable encodings and fitness functions for specific information processing and communication tasks. Students will be able to design neural network and develop reinforcement learning techniques by using simulation tools and Java programming.
Learning Outcomes
- To define concept, methods and architectures typical for machine learnings
- To explain how machine learning operate and basic purpose
- To apply knowledge about machine learning for telecommunication services
- To analyze functions of machine learned components, as well as their interactions in order to find appropriate solution
- To analyze organization of machine learned model
- To define basic components for realisation of needed function by machine learning
- To create machine learned models including various types of recognition and self-adaptation
- To evaluate and assess solutions based on different methods of machine learning
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
Providing an overview of the basic principles of multiagent paradigm. Acquainting students with formal approaches to multiagent system specification, knowledge representation, behaviour modeling and interagent communication in order to solve problems related to distributed artificial inteligence.
Learning Outcomes
- Discuss the notions of the intelligent agent and multi-agent system
- Distinguish basic categories of agents and multi-agent systems
- Identify the basic application areas of intelligent agents and multi-agent systems
- Apply basic multi-agent paradigms to the real world problem solving
- Employ the basics of the game theory to formulate and solve multi-agent problems
- Construct simple but functional multi-agent systems
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
Knowledge about complex neural network controller design. Knowledge about different learning and automated setting methods for parameters tuning of intelligent control algorithms.
Learning Outcomes
- Classify artificial neural networks
- Apply nonrecursive neural network learning algorithms
- Apply recursive neural network learning algorithms
- Identify nonlinear dynamical systems via static neural networks
- Design a neural controller for control of nonlinear dynamical systems
- Apply genetic algorithms in optimization
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
The course gives students knowledge and skills for solving medium to hard problems from diverse expert systems application domains.
Learning Outcomes
- Define and describe expert system and its main constituents.
- Distinguish class of problems suitable for solving with expert systems.
- Breakdown the problem and select crucial parts.
- Assemble various parts of knowledge and skills in order to devise the approach to solution.
- Design and create expert system suitable for solving particular problem.
- Appraise the quality of solution and justify the employed techniques.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
Deep learning is a branch of machine learning based on representation of data with complex representations at a high level of abstraction. These representations are achieved by a sequence of trained non-linear transformations. Deep learning methods have been successfully applied in many important artificial intelligence fields such as computer vision, natural language processing, speech and audio understanding as well as in bioinformatics. This course introduces the most important deep discriminative and generative models with a special focus on practical implementations. Part one introduces key elements of classical feed-forward neural networks and overviews basic building blocks, regularization techniques and learning procedures which are specific for deep models. Part two considers deep convolutional models and illustrates their application in image classification and natural language processing. Part three considers sequence modelling with deep recurrent models and illustrates applications in natural language processing. Finally, part four is devoted to generative deep models and their applications in vision and text representation. All concepts are followed with examples and exercies in a modern dynamic language (e.g. Python, Lua, Julia). Most exercises shall be implemented in a suitable deep learning application framework (e.g. Tensorflow and Torch).
Learning Outcomes
- Explain advantages of deep learning with respect to the alternative machine learning approaches.
- Distinguish techniques which enable successful training of deep models.
- Explain application fields of deep discriminative and generative models.
- Distinguish kinds of deep models which are appropriate in supervised, semi-supervised and unsupervised applications.
- Apply deep learning techniques in understanding of images and text.
- Analyze and evaluate the performance of deep models.
- Design deep models in a high-level programming language.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: English and Croatian
Students will deeply understand concepts of concurrency and parallelism in systems and networks. They will gain practical knowledge in concurrent and distributed programming, including concepts of processes and threads, as well as inter-process communication and multithread mechanisms. Students will have practical skills to implement concurrent and distributed applications by using programming languages Java and Erlang.
Learning Outcomes
- Recognize different types of concurrency
- Explain concurrency mechanisms
- Apply concurrency mechanisms in software
- Analyze different problems in concurrent programs
- Combine different mechanisms and develop program
- Compare good and bad properties of some solution
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
The purpose of this course is to serve as an introduction to and an overview of the field of biomedical engineering. Considering this purpose, a link between biomedicine and electrical engineering will be given. Students should be able to understand and define the discipline of biomedical engineering, basic physiological and electrophysiological mechanisms, basic bioelectric signals, electrodes and registration techniques. They will also acquire an introductory knowledge about the most important diagnostic and therapeutic electromedical equipment and safety assurance in medical facilities.
Learning Outcomes
- Eplain functioning of diagnostic medical imaging systems
- Appraise applicability of the particular medical imaging method
- Describe physiological systems in the human body
- Distinguish main features of biomedical signals
- Describe biomedical signal registration techniques
- Compare biomedical signals registration and analysis methods for the particular problem solving
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
Students will gain advanced knowledge on both organization and structure of present-day computer networks and especially the Internet. After successfully completing the course they will be able to configure network resources thus targeting efficient interworking of various distributed resources. They will also be able to apply advanced security techniques and build virtual private networks (VPNs) and to implement advanced interconnection of distributed applications over the standard Internet platform.
Learning Outcomes
- Plan the interworking of distributed application basing on Semantic Web technology
- develop and evaluate distributed application architectures according to functional requirements
- Design IPv6 based computer networks
- Select the transport protocol appropriate for a given application
- Develop a suitable security framework for a particular network environment
- Select appropriate quality of service mechanisms for a give computer network
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
This course is a continuation of Algorithms and Data Structures undergraduate course and it is aimed to students wishing to widen and deepen the understanding of most important algorithms and data structures. Algorithms needed in various applications are presented, with the emphasis on understanding of their underlying logic and theoretical foundation. Specific program solutions are analyzed during laboratory exercises. By adopting the subject, the students will notably widen their competences and abilities to solve more complex problems that require correct selection and an efficient implementation of different algorithms.
Learning Outcomes
- Recognize and analyze the problem to be solved by computer
- Relate the specified problem to the one of the same kind or a similar known and already solved problem
- Select the best algorithm for a known problem
- Design one’s own algorithm for an unknown problem
- Assess the validity of one’s own solution, in the sense of algorithm convergence and complexity
- Compare and contrast own solution to possible alternatives
- Recommend the algorithm for an unknown problem on the basis of analysis of alternatives
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
Introduction. Systems and programming description management. Lambda and Kappa architectures for big data. Basic principles and features of big spatial and spatio-temporal data. Modelling of spatial and spatio-temporal data. Specification of relevant operations on spatial and spatio-temporal data. Indexing. Global and local indexes. Static and dynamic indexes. Geohashes. Spatio-temporal data streams. SQL-based analysis of spatio-temporal data streams within integrated big data platforms. Implementation of data types and operations in object-functional programming language and distributed dataflow platforms. Implementation based on API of integrated platform for distributed batch and data stream processing. Development of user-defined functions. Specification of spatial and spatio-temporal queries in SQL-like query languages. Data mining of big spatio-temporal data.
Learning Outcomes
- Identify fundamental features of spatial and spatio-temporal big data
- Identify fundamental features of spatioto-temporal data streams
- Design and implement spatial and spatio-temporal data types in object-functional programming language and distributed data flow platforms
- Develop simple algorithms for big spatio-temporal data management
- Develop simple algorithms for spatio-temporal data streams management
- Develop spatial and spatio-temporal queries using SQL-like expressions
- Develop simple algorithms for spatio-temporal data mining and knowledge discovery.
- Choose big data management technologies in spatio-temporal application domain
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
Introduction to mining of massive data sets. The MapReduce programming model. Finding similar items. Mining data streams. Link analysis. Finding frequent itemsets. Clustering of massive data sets. Recommendation systems. Mining social-networks graphs. Advertising on the Web. Dimensionality reduction. Large-scale machine learning.
Learning Outcomes
- Recognize and understand why certain problem belongs to Big Data category
- Apply the MapReduce programming model when faced with certain problems in practice
- design and evaluate system for finding similar items in a massive data set
- design and evaluate system for finding frequent itemsets in a massive data set
- design and evaluate system for node rank among graph represented massive data set
- design and evaluate recommendation system
- apply the appropriate clustering algorithms in order to identify clusters in a massive data set
- apply the appropriate algorithms for processing data streams
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
Students will be able to design and implement databases consisting of semistructured, multimedia and spatio-temporal data. The acquired knowledge will be applied on projects and applications.
Learning Outcomes
- Design object-relational, temporal, spatial and NoSQL databases
- Use object-relational, temporal, spatial, stream and NoSQL databases
- Interpret semi-structured and structured data
- Explain the concepts of different data models
- Explain the principle of data warehouse development
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
Learning Outcomes
- Explain the principles of quantum mechanics
- Apply Dirac notation in simple calculations in quantum mechanics
- Identify the state of the quantum bit on the Bloch sphere
- Describe the quantum key distribution protocol BB84
- Explain entanglement in a qantum systems
- Describe the Deutsch and Shor quantum algorythms
- Outline the basic features of candidate technologies for physical realization of quantum computers.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: English and Croatian
Teach elementary and high-school students basics of robotics.
Type of methodology: Hands-on
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Language: Croatian
Teach students basic of programming and robotics.
Type of methodology: Hands-on
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Language: Croatian
Explain to students the architectures and principles of service-oriented computing and cloud computing. Introduce students to the requirements and methods for data discovery and analysis. Present the utilisation of service environments, tools, and programming technologies for data analysis in business, research, industry and other application domains.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: Croatian
Introduce students to theoretical and practical knowledge in the field of data visualisation. Teach them how to use and to work with data visualisation tools and libraries. Train them to work individually and within team on data visualisation projects, and enable critical thinking and evaluation of data visualisation.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: Croatian
Provide students with insight and basic knowledge of the properties, prerequisites and ways of establishing, using and evaluating distributed computer systems, parallel systems and service-oriented systems. Show opportunities and explain the basics of using system and software tools, and develop applications in distributed and service-oriented computing environments.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++; MPI; OpenMP
Language: Croatian
Enable students to become familiar with and include them in the processes of development, improvement and application of the environmentally friendly computing technologies. Demonstrate to students the scope and amount of impact of computer systems on the environment and present them ways to recognize the potential of green progress in computing technologies. Provide students with current knowledge of (energy) efficient hardware and software technologies.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++; HPC tools (e.g. profiler, scheduler)
Language: Croatian
This course provides the necessary mathematical background for understanding and implementing neural networks, genetic algorithms and fuzzy systems. The course introduces case studies to students where neural networks, genetic algorithms, and fuzzy logic are implemented in solving problems in the area of optimisation, pattern recognition, automatic control, and expert systems.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: Croatian
Provide students with practical knowledge in computer network design. Through lectures and exercises, train them for user requirement analysis, design, planning, configuration, implementation, analysis and debugging of a computer network. Introduce students to legal and technical regulations related to planning and construction. Special emphasis will be placed on project documentation, cost list, configuration files for network devices (computers for special purposes), their implementation and maintenance. Introduce students to practical approach in quality of service implementation in a specific network environment.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: No prerequisite knowledge
Language: Croatian
Explain, demonstrate, develop and use models, processes, tools and computing environments for resource planning and management, and determine the performance of the hardware and software part of embedded, distributed, service, mobile, and other computing systems, environments, and related software solutions.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++;
Language: Croatian
To enable students to research in the field of architecture and communication within multi-processor systems. Getting familiar with parallel troubleshooting and parallel algorithms. Acquire Skills in designing parallel processing programs and working with multiple processor and parallel architecture operational systems. To enable students to use CUDA and GPGPU technology.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++; HPC tools (e.g. profiler, scheduler)
Language: Croatian
Introduction to the principles and methods of machine learning. Introduction to deep learning methods. Introduction to the architecture of deep neural networks, learning algorithms and possible application of deep learning. Learning appropriate skills with software tools and cloud services which enable the development of complex models and deep learning.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: Croatian
Computer Environments and Algoritms for Data AnalysisUNIOS-FERITExplain, demonstrate, develop and use distributed and service computing systems, procedures, and tools to effectively analyze large datasets in business, research, industrial, and other applications.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: Croatian
To teach students to design, analyze and implement scalable software for execution on high-performance computer systems and to adapt software solutions of a scientific nature to work on actual parallel computer systems.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: Yes, for some only
Participants prerequisite knowledge: C/C++
Language: Croatian
At the workshop, participants will have the opportunity to practically test and test the working of deep neural networks. Workshops will be conducted using browsers and on the cloud provided by NVIDIA. The workshops will include state-of-the-art graphics accelerators and some of the latest freely available software tools and libraries in the field of machine learning. The workshop is organized by the Deep learning institute of NVidia.
Type of methodology: Combination of lecture and hands-on
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
High performance computing
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Python
Language: Croatian
Machine learning; Deep learning
Type of methodology: Lecture
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Python
Language: Croatian
Presentation of HPC resources / capabilities. Potential applications / use cases. Hands-on workshop that deals with how to connect to an HPC and run simple tasks. Runing simple HPC tasks / problems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Presentation of HPC resources / capabilities. Potential applications / use cases. Hands-on workshop that deals with how to connect to an HPC and run simple tasks. Runing simple HPC tasks / problems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Presentation of HPC resources / capabilities. Potential applications / use cases. Hands-on workshop that deals with how to connect to an HPC and run simple tasks. Runing simple HPC tasks / problems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
The main aim is introduction to computer cluster environment and advanced usage of job management system - job description and management. In addition following advanced topics are covered: using virtual large memory system based on ScaleMP vSMP technology, GPUs utilization and using Singularity containers.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Linux basics
Language: Croatian
To advance understanding of High Performance Computing architectures and teach students to design and optimize algorithms suitable for best performance.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The students will become familiar with fundamental techniques of Monte Carlo and Molecular Dynamics are used in (primarily classical) simulations to understand and predict properties of microscopic systems in materials science, physics, biology, and chemistry. A simulation project composed of scientific research, algorithm development, and presentation is required.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The numerical methods, formulation and parameterizations used in models of the circulation of the atmosphere will be described in detail. Widely used numerical methods will be the focus but we will also review emerging concepts and new methods.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
This course introduces the modern genomics and systems biology practices to students of a wide background. The course aims to introduce biological concepts and methods to computer scientists as well as core mathematical and programming skills to life sciences graduates. In doing so, an integration of these disciplines will be possible with the aim of advancing the research frontier helping to solve the most challenging health and environmental problems of our times.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The students will apply high-performance computing to solve physics problems. Participation in the lab course is required.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Computer Graphics is an innovative course which will expose students to cutting edge graphics and animation technologies, interactive media design principles, and development methodologies.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Designing and implementing larger software projects using object-oriented methods.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
To teach students the theoretical concepts on deep learning and how to implement and use them to automatically extract features from data and build prediction models for several applications. Methods covered include feedforward, convolutional, recurrent and recursive networks.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The students will learn fundamental issues in design and development of parallel programs for various types of parallel computers. They will also be introduced to visualization techniques useful in analysis of engineering and scientific data.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
To educate the enrolled students in established numerical methods used in computational science. To bring them into contact to state-of-the-art numerical methods being used currently in cutting-edge computational science research.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Introduce students to data science, big data analysis and statistics. This includes a focus on statistical methods for data scientists, including random variables, probability theory, continuous and discrete distributions, inference, estimation, hypothesis testing and statistical significance. To develop a set of practical skills and tools in terms visualizing, exploring, storing and processing data, and an introduction to cluster-computing frameworks (Hadoop, Spark).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Provide the students with the necessary computer programming and software engineering background to solve complex problems by numerical methods. Introduce the basic concepts in high performance computing (HPC), understand modern computer architectures, optimization strategies and the parallel programming. Cluster, grid and cloud computing will be introduced.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The scope of the course is to provide training in state-of-the-art numerical techniques for research in lattice Quantum Chromodyanmics (LQCD). This course aims at teaching the PhD students to perform large scale simulations on peta scale supercomputers and prepares them for developing codes for exascale computing.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The aim of this course is to provide a broad introduction to students on both theoretical as well as practical concepts in machine learning, data mining and pattern recognition. Topics include fundamental machine learning concepts and algorithms, such as supervised learning (parametric and non-parametric algorithms, classification and regression, discriminative and generative learning), unsupervised learning (clustering, dimensionality reduction, data imputation), and learning theory (bias-variance tradeoff, curse of dimensionality). The course will also include an introduction to deep learning, practical advice for designing machine learning systems, as well as an overview of modern scientific applications of machine learning and data mining (e.g., classification of omics data and applications in biology, object detection and human behaviour analysis, weather forecasting).
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Introduce mathematical tools and algorithms used in computational sciences focusing on methods used in numerical simulation and data analysis.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
To teach students to use simulation algorithms and to analyze their results in order to study complex systems.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
The scope of the course is to provide training in state-of-the-art numerical techniques for research in Turbulence and Complex Flows. This course aims at teaching the PhD students to perform large scale simulations on peta scale supercomputers and prepares them for developing codes for exascale computing.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Train students in managing large data sets of various forms, understand their structure and common methods to manipulate them and apply techniques for their visualization. Applications from computational sciences will be used as a demonstration of visualization of scientific data.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: No prerequisite knowledge
Train participants in the basic aspects of HPC and how to access and use computational resources.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: No
Paid training activity for participants: Yes, for all.
Participants prerequisite knowledge: C/C++ AND/OR Fortran
Train participants in more advanced aspects of HPC inlcuding, Hybrid Programming and Python for HPC.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes, if requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: OpenMP
Train participants in advanced aspects of HPC such as GPU programming, AI techniques, Simulation and Modelling.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes, if requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: MPI; OpenMP
Train participants in packages/software used by industry.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes, if requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Hands on sessions where HLST member help research groups with their computational projects.
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes, if requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Attendees will receive hands-on training on how to remotely access and use the National Competence Center infrastructure, how to run their codes efficiently on the infrastructure, basics of parallel programming, and guidance on how to write a competitive proposal for computational resources. This is crucial for helping user communities in Cyprus to capitalise on EuroHPC opportunities, such as access to EuroHPC leadership computers, participation in EuroHPC research and innovation calls and collaboration with other EU members of EuroHPC.
Full event details can be found in the link below:
https://castorc.cyi.ac.cy/events/hpc-beginner-training-event-02-2021
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: MPI; OpenMP
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Knowledge of a programming language
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++; Fortran
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: MPI; Domain-specific background knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Python
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: C/C++
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Lecture
Participants receive the certificate of attendance: If requested
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Python
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge:
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: Numerical methods (linear algebra, statistics)
Type of methodology: Hackathon
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: In-depth knowlegde of own code
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge
Type of methodology: Combination of lecture and hands-on
Participants receive the certificate of attendance: Yes
Paid training activity for participants: No, it's free of charge
Participants prerequisite knowledge: No prerequisite knowledge