Computer Science (CS)
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The Computer Science Modules are dedicated to design and implementation of larger software projects using object-oriented methods. Students shall be enabled to either apply virtualization technologies in the context of GRID and cloud computing or to master the basic concepts of High Performance Computing which are needed for using modern (super-)computers. The Computer Simulation path is divided into 2 modules, one in the first semester, the other in two next semesters (during first three semesters):
1st (Winter) Semester - Computer Science 1 (shortly - CS1)
Workload: 270 hours (1 semester)
ECTS credits: 9 ECTS
Term: Winter (1st semester)
Repeatablity: not restricted in attempts
Final assessment: written 120-minutes examination or oral 30-minutes examination (The type of the final module exam is announced at the beginning of the lecture period; exam is counted as 9 ECTS)
Pre-requisites for the final exam: Knowledge of one programming language is assumed. Students should visit 2 components of this module out of 3: CS1-a (Modern Programming) is the obligatory component for all; students can choose the second component between CS1-b (Virtualization I) and CS1-c (Introduction to High Performance Computing).
Description of the module: Acquisition of knowledge to design and implement larger software projects using object-oriented methods. Students shall be enabled to either apply virtualization technologies in the context of GRID and cloud computing (for the choice Virtualization I) or to master the basic concepts of High Performance Computing (HPC) which are needed for using modern (super-)computers.
Components of CS1 module:
CS1-a. Modern Programming
Teaching format: Lectures and exercises
Weekly hours: 4 (180 hours in total)
Assessment: assessment folder with 2 components; CS1-a is obligatory
Contents: Unified Modeling Language, C++, debugging, Makefiles, design patterns, GUIs.
CS1-b. Virtualization I
Teaching format: Lectures and exercises
Weekly hours: 3 (90 hours in total)
Assessment: assessment folder with 2 components; CS1-b is one of the options
Contents: Introduction to virtualization and its application in Science. Topics covered are general virtualization technologies like hypervisors, paravirtualization and Operating-system-level virtualization, containers as well as their usage in the context of scientific Grid and Cloud Computing. Hands-on training on a selected topic.
CS1-c. Introduction to High Performance Computing (HPC)
Teaching format: Lectures
Weekly hours: 2 (90 hours in total)
Assessment: assessment folder with 2 components; CS1-c is one of the options
Contents: History of HPC; Overview; Processor; Memory; Networks; Parallel computing; Performance; Numerical methods; Future computing; Repetition.
2nd (Summer) Semester - Computer Science 2 (shortly - CS2)
Workload: 180 hours (1 semester, for BayesLearn) or 90 hours (for CS2-a)
ECTS credits: 7 ECTS for BayesLearn; 4 ECTS for CS2-a*
Term: Summer (for CS2-a component; for whole BayesLearn)
Repeatablity: not restricted in attempts
*CS2-a do not count independently without the second component of the assessment folder.
Final assessment:
• For CS2: no assessment in summer semester (one component of the assessment folder with 2 components).
• For BayesLearn: written 120-minutes examination or oral 30-minutes examination (The type of the final module exam is announced at the beginning of the lecture period; exam is counted as 7 ECTS)
Pre-requisites for the final exam:
• For CS2: Students should visit 2 components of CS2 module out of 3; CS2-a (Tools) is the obligatory component for all students.
• For BayesLearn: A background in numerical methods is suggested.
Description of the CS2 module: Ability to use different tools for software engineering. Acquisition of the basics of image processing in general and image analysis of tomographic images in particular or
ability to set up orchestration environments and apply them.
Description of the BayesLearn module: The students have a profound understanding of Bayesian estimation and are able to apply standard estimators (MLE, MAP, etc.) for different use cases such as supervised machine learning models or inverse problems. This allows them to derive uncertainty measures in prediction and inference. They are able to select appropriate prior distributions. Sampling via Monte Carlo and Markov Chain Monte Carlo is understood in theory and practice. Students are able to independently evaluate the quality of a developed model and can implement all important algorithms, while being able to additionally apply probabilistic programming techniques to simplify the implementation. Beyond that, students are able to design and apply hierarchical and graphical models and have a working knowledge in Bayesian Neural Networks.
Components of the summer semester modules:
CS2-a. Tools
Teaching format: Lectures and exercises
Weekly hours: 2 (90 hours in total)
ECTS credits: 3 points
Assessment: assessment folder with 2 components; CS2-a is obligatory
Contents: Version control systems, computer algebra packages, script languages, unit testing, Fortran, combining different programming languages, profiling, numerical libraries, important data structures (trees, hash tables).
BayesLearn. Bayesian Learning
Teaching format: Lectures and exercises
Weekly hours: 4 (180 hours in total)
Assessment: written 120-minutes examination or oral 30-minutes examination (The type of the final module exam is announced at the beginning of the lecture period; exam is counted as 7 ECTS)
Contents: Bayesian estimation (Bayesian formula, MLE, MAP, etc.), prior distributions, data-driven models / regression, sampling methods (Monte Carlo, MCMC, etc.), inverse problems, model evaluation and comparison, probabilistic programming, hierarchical models, graphical models, Bayesian neural networks. Students are expected to know traditional machine learning techniques and have solid to advanced knowledge in analysis and linear algebra, and a good working knowledge in probability. A background in numerical methods is suggested.
3rd (Winter) Semester - Computer Science 2, continued (shortly - CS2)
Workload: 120 hours (for IMG2-a/CS2-c)
ECTS credits: 3 ECTS*
Term: Winter (3rd semester - for IMG2-a/CS2-c)
Repeatablity: not restricted in attempts
* IMG2-a/CS2-c do not count independently without the first component of the assessment folder.
** Students who chose Bayesian Learning in the 2nd (summer) semester, do not need to attend this module.
Final assessment: written 120-minutes examination or oral 30-minutes examination (The type of the final module exam is announced at the beginning of the lecture period; the module final examination is taken in connection with both module components).
Pre-requisites for the final exam: Students should visit 2 components of CS2 module out of 3; CS2-a (Tools) is the obligatory component for all students. Students of all specializations, except Imaging in Medicine, can choose the second component between IMG2-a (Image Processing and Data Visualization) and CS2-c (Virtualization II). Students of Imaging in Medicine specialization must visit both IMG2-a and CS2-c components in the 3rd semester.
Description of the CS2 module: Ability to use different tools for software engineering. Acquisition of the basics of image processing in general and image analysis of tomographic images in particular or ability to set up orchestration environments and apply them.
Components of the winter semester modules:
IMG2-a. Image Processing and Data Visualization
Teaching format: Lectures and exercises
Weekly hours: 3 (120 hours in total)
ECTS credits: 4 points
Assessment: assessment folder with 2 components; IMG2-a is one of the options for all students, except for IMG-specialization. For students of IMG-specialization this components is obligatory
Contents: Introduction to the importance of modern image processing and data visualization techniques to brain imaging; Data types and structures (scalar, vector, volume data); Transformation and filtering techniques to carve out specific image features; Image processing pipelines in a supercomputing environment; Impact of AI on image processing; Methods for brain data visualization.
CS2-c. Virtualization II
Teaching format: Lectures and exercises
Weekly hours: 3 (120 hours in total)
ECTS credits: 4 points
Assessment: assessment folder with 2 components; CS2-c is one of the options for all students, except for IMG-specialization. For students of IMG-specialization this components is obligatory
Contents: Additional training on virtualization techniques including orchestration of containerized environments. Protocols in virtualized environments and their usage. Hands-On training with project.
Last modified: 26.05.2026
