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

1. Introduction to the biomechanical concepts.
2. Biomechanics of cell membrane and form of cell.
3. Biomechanics of human tissues in organism.
4. Human locomotion – system of bone muscles.
5. Thermomechanics of muscle contraction.
6. Active motion of joints.
7. Forces on the skeleton.
8. Visco-elestic properties of body liquids.
9. Heart as a pump machine.
10. Hearing biomechanics.
11. Breathing mechanics.
12. 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)