Teaching

The institute is involved in academic teaching at the undergraduate and graduate level. Courses cover core topics in bioinformatics, systems biology, and data analysis, and are closely connected to current research activities. Teaching formats range from lectures and seminars to project-based modules and practical training.

For all courses, if in doubt about registration procedures etc., please contact your examination office, to make sure you’re all setup to receive the credits for successfully completing the course.

Courses by Semester

Description:
The module provides an introduction to graph theory with a particular focus on applications in biomedicine. It covers biological networks, methods of statistical network analysis, and graph-based machine learning. The goal is to develop an in-depth understanding of how to model and analyze biomedical data using networks. Upon completion of the module, students will be able to apply common tools in network biology, critically evaluate analysis results, and independently develop new graph-based approaches.

Organization and Expectations:

  • Target audience: students in the master programs Computer Science, Data Science, Biology, Biotechnology, AIMS, CoDaS
  • Format: lectures & exercises
  • Registration: direct entry via Stud.IP, available from March 2, 2026
  • First lecture: Thursday, April 9, 2026, 16:45 – 18:15, BRICS R046

Description:
The course provides students with the skills to design and implement small to medium software projects and analytical workflows in a team, with a focus on statistics and machine learning. Students will learn how to work with real-world data in healthcare and biomedical fields, applying machine learning algorithms to analyze and interpret data. By the end of the course, students will be able to critically evaluate machine learning workflows and effectively use them to solve complex data science problems.

Please not that this is not a Python course and we expect you to either already know Python, or learn Python independently within the first few weeks of the course.

Organization and Expectations:

  • Target audience: students in the master programs Data Science or AIMS
  • Format: mostly independent work, poster session, group work, group presentations
  • Registration: waiting list via Stud.IP, available from March 2, 2026
    Please note that available seats are limited and we prioritize students in the programs mentioned above and in higher semesters. Students who haven’t completed the module yet are prioritized over students trying to improve their grade.
  • Mandatory kick-off meeting: Thursday, April 9, 2026, 15:00 – 16:30, BRICS R044 

Description:
SciMOW introduces key aspects of the scientific process, including the philosophy of science, good scientific practice, research ethics, project planning, scientific reading and writing, code documentation, and how to effectively pitch a scientific topic. The course culminates in a graded assignment: an essay on a data science-related topic.

Organization and Expectations:

  • Target audience: M.Sc. Data Science
  • Format: inverted classroom with mandatory hands-on sessions 
  • Registration: waiting list via Stud.IP, available from March 2, 2026
    Please note that available seats are limited and we prioritize students in the programs mentioned above and in higher semesters. Students who haven’t completed the module yet are prioritized over students trying to improve their grade.
  • Mandatory kick-off meeting: Thursday, April 9, 09:45 – 10:15, UP 2.512 

Description:

This course builds on BIBS I and introduces Master’s students in Computer Science to advanced topics in bioinformatics and systems biology. Students deepen their understanding of omics data, including concepts such as alternative splicing and epigenetic regulation. They learn how to construct and analyze metabolic models, identify biomarkers, and develop systems medicine approaches—while also learning to critically evaluate these methods.

Organization and Expectations:

  • Target audience: M.Sc. Computer Science
  • Format: lectures & exercises
  • Registration: direct entry via Stud.IP, available from March 2, 2026
  • First lecture: Thursday, April 9, 13:15 – 14:45, BRICS R046

Description:

The students learn about basic programming in Python. The course covers everything from data types and useful modules to regular expressions. The students are given many exercises to practice their programming skills and a final project according to their level. Additionally, students will be introduced to versioning with git.

Organization and Expectations:

  • Target audience: students in the master programs Biology, Biotechnology
  • Format: mandatory hands-on sessions
  • Registration: waiting list via Stud.IP, available from March 2, 2026
  • Mandatory kick-off meeting: Thursday, April 9, 08:00 – 09:30, BRICS R044 

Students are assigned a specific paper and have ample time to prepare different presentations based thereon: a method presentation focusing on introducing and explaining a specific data science method related to the assigned paper; a scientific poster presenting the paper, a paper presentation, focusing on the paper itself as a whole and also providing a bit broader context.

Organization:

  • Target audience: students in the bachelor or master programs Computer Science, Data Science, Business Informatics, CoDaS
  • Format: mostly independent work, poster session, individual presentations
  • Registration: only possible via the seminar registration process of the Computer Science programs.
  • Mandatory kick-off meeting: Wednesday, April 8, 2026, 13:15 – 14:45, BRICS R046

Description:

This course is designed for students of Computer Science and Business Informatics. It offers a collaborative space for discussing and working through mathematical problems from courses such as Discrete Mathematics, Calculus, and Linear Algebra. Organized as a supervised study group, students can sign up, bring their questions, and form thematic groups to work on problems together. The focus is on peer learning, supported by guidance when needed.

Organization and Expectations:

  • Target audience: students in the bachelor programs Computer Science, Business Informatics
  • Format: supervised study group
  • Registration: please book the specific sessions you want to attend in the corresponding Stud.IP course, available from March 2, 2026

Organization:

Topic Presentation: Tuesday, 27 January 2026, 11:30 a.m. – 1:00 p.m., PK11.1
As part of the topic presentation, the supervisors briefly present the core idea and motivation for each project on offer. We also provide general information about the SEP process.

Themen: 

Background: 

  • Traditional 3D house modeling relies on manual CAD workflows, which are slow and require expert knowledge.
  • Converting 2D architectural plans into 3D geometry is labor-intensive and prone to human error.
  • Semi-automatic tools still require major cleanup and struggle with incomplete inputs.
  • Growing demand for rapid prototyping and 3D printing motivates automated reconstruction.

Tasks: 

  • Develop an AI-assisted pipeline that converts 2D floor plans into a rough 3D geometry.
  • Integrate an AI model for automatic segmentation of walls, doors, and windows from 2D floor plans.
  • Implement mesh post-processing steps (cleaning, smoothing, merging, scaling) using Python libraries.
  • Convert the refined 3D mesh into a 3D-printable stereolithography (.stl) file.
  • Design and implement a simple web interface for uploading plans and downloading the generated STL result.
  • Optional: Support multiple input views or combine front/back elevation plans to improve reconstruction accuracy.

Organizational Aspects:

  • Required knowledge:
    • Backend: FastAPI (Python)
    • AI Model: SAM (Segment Anything Model) for floor plan segmentation
    • Image Processing / Floor plan parsing: OpenCV (Python library)
    • Mesh Processing: Open3D, Trimesh (Python libraries)
    • Frontend: React (JavaScript)
  • Group size: 2 Groups
  • Maximum number of students per group: 7
  • Language: English
  • Supervisor: Dr. Emetis Niazmand »
     

Hintergrund: 

  • Wer ist dran?
  • Wieviele Bälle sind noch auf dem Tisch?
  • Welcher Ball ist der nächste?
  • Wie entwickelt sich mein spielerisches Können?
  • Wer gewinnt die meisten Spiele übers Jahr?
  • Wie schnell ist mein Anstoß?
  • Wie sollten Spiel- und Objektball idealerweise laufen?

Ziel: Eine App die:

  • es erlaubt, das Spiel inkl. ”Punktestand” zu verfolgen
  • Statistiken pro Spielter speichert, berechnet, und visualisiert
  • die Anstoßgeschwindigkeit über Bild- oder Tonerkennung bestimmen kann.
  • Balllaufwege (in AR oder schematisch) einblendet

Organisatorisches:

  • Hilfreich:
    • Interesse an Billard
    • Interesse an App-Entwicklung
  • Gruppenzahl: 1 Gruppe
  • Teilnehmer pro Gruppe max.: 10
  • Sprache: Deutsch oder Englisch
  • Programmiersprache: Android SDK, Kotlin o. Java o. C++
  • Betreuer: Prof. Dr. Tim. Kacprowski »

Background

  • Working with background music is common, but content selection is often uninspired

  • Existing solutions are either fully algorithmic or lack social interaction

  • HumanMusic combines:

    • personalized radio

    • watch-together concepts

    • collaborative recommendation within a (research) group

Goal

  • Web application for a private continuous radio stream

  • Core content:

    • Playlist of YouTube videos submitted by authorized users

  • AI-voiced “AD breaks” between tracks:

    • General news from the institute or research group

    • Short summaries of recent publications

    • Announcements from the institute’s communication system

  • Optional:

    • Individual channels for regular contributors

Organizational Aspects

  • Frontend: HTML, CSS, JavaScript

  • Backend: Node.js, PHP, or Python

  • Example Text-to-Speech Tool: Qwen3-TTS » 

  • Helpful:

    • Interest in web development

    • Experience with WebSockets and local Text-to-Speech models

  • Groups: 1

  • Participants max.: 5

  • Language: German or English

  • Supervisor: Matthias Overmann »

Description:

The Python Lab is a Master’s level course designed for students in Data Science and Artificial Intelligence in Molecular Sciences. The course provides students with the skills to design and implement small to medium software projects and analytical workflows, with a focus on statistics and machine learning. Students will learn how to work with real-world data in healthcare and biomedical fields, applying machine learning algorithms to analyze and interpret data. By the end of the course, students will be able to critically evaluate machine learning workflows and effectively use them to solve complex data science problems.

Organization:

  • entry via Stud.IP
  • Poster presentation and group project 
  • Kick-off meeting: 30.10.2025 @ 15:00 in R044 BRICS

Description:

This course introduces Bachelor students in Computer Science to core concepts in molecular biology and bioinformatics. Students learn the fundamentals of molecular biology and become familiar with experimental methods for determining genome sequences and transcript abundances. The course covers classical bioinformatics problems such as sequence alignment, along with statistical methods for analyzing associations between genomic variants and phenotypes or identifying differentially expressed genes.

Organization:

  • entry via Stud.IP
  • Lecture and Exercise
  • Kick-off meeting: 23.10.2025 @ 11:30 in R107/108 BRICS

Organization:

Description:

This course equips students with the skills to clearly and effectively communicate scientific ideas in writing, presentations, and visualizations. Students will learn how to structure scientific texts, follow established guidelines, and practice writing a short review article. They will also explore how to design clear and engaging presentations through several short assignments, including a creative peer exercise. A further focus is placed on visualizations as an essential part of scientific communication: students will study principles of perception and design, learn how to map data to visual variables, and practice creating and critiquing effective figures.

The course is highly interactive and practice-oriented. Students work both individually and in groups, receive feedback on their work, and engage in peer discussions. By the end, participants will be able to write concise scientific texts, design meaningful visualizations, and deliver impactful presentations.

 

Organization:

  • Lecturers: Prof. Dr. Tim Kacprowski, Prof. Dr. Thomas Deserno, and Prof. Dr. Steffen Oeltze-Jafra

  • Classification: Bachelor and Master

  • Language: English

  • Regular Dates: Weekly

  • Entry via Stud.IP

  • First session: 28.10.2025 @ 15:00 in R046 BRICS

Description:

This course is designed for students of Computer Science and Business Informatics. It offers a collaborative space for discussing and working through mathematical problems from courses such as Discrete Mathematics, Calculus, and Linear Algebra. Organized as a supervised study group, students can sign up, bring their questions, and form thematic groups to work on problems together. The focus is on peer learning, supported by guidance when needed.

Organization:

  • Please book an appointment in Stud.IP
  • First session: 30.10.2025 @ 11:30 in R107/108 BRICS

Description:
The module provides an introduction to graph theory with a particular focus on applications in biomedicine. It covers biological networks, methods of statistical network analysis, and graph-based machine learning. The goal is to develop an in-depth understanding of how to model and analyze biomedical data using networks. Upon completion of the module, students will be able to apply common tools in network biology, critically evaluate analysis results, and independently develop new graph-based approaches.

Organization:

  • entry via Stud.IP
  • This course is intended for master’s students of Computer Science, Data Science, Biology, and Biotechnology.
  • Lecture & Exercise
  • Kick-off meeting: 

Description:
The Python Lab is a Master’s level course designed for students in Data Science and Artificial Intelligence in Molecular Sciences. The course provides students with the skills to design and implement small to medium software projects and analytical workflows, with a focus on statistics and machine learning. Students will learn how to work with real-world data in healthcare and biomedical fields, applying machine learning algorithms to analyze and interpret data. By the end of the course, students will be able to critically evaluate machine learning workflows and effectively use them to solve complex data science problems.

Organization:

  • entry via Stud.IP
  • Poster presentation and group project 
  • Kick-off meeting: 

Description:
SciMOW is a practical course designed for Master’s-level Data Science students. It introduces key aspects of the scientific process, including the philosophy of science, good scientific practice, research ethics, project planning, scientific reading and writing, code documentation, and how to effectively pitch a scientific topic. The course culminates in a graded assignment: an essay on a data science-related topic.

 

Organization:

  • Lecture and seminar 
  • mandatory attendance 
  • Kick-off meeting: 

Description:

This course builds on BIBS I and introduces Master’s students in Computer Science to advanced topics in bioinformatics and systems biology. Students deepen their understanding of omics data, including concepts such as alternative splicing and epigenetic regulation. They learn how to construct and analyze metabolic models, identify biomarkers, and develop systems medicine approaches—while also learning to critically evaluate these methods.

Organization:

  • entry via Stud.IP
  • Lecture and Exercise
  • Kick-off meeting: 

Description:

This is a course for Biologists and Biotechnologists on a Master’s level. The students learn about basic programming in Python. The course covers everything from data types and useful modules to regular expressions. The students are given many exercises to practice their programming skills and a final project according to their level.

Organization:

  • entry via Stud.IP
  • Lecture and project work
  • Kick-off meeting: 

Organization:

  • entry via Stud.IP
  • Kick-off meeting: 

Description:

This course is designed for students of Computer Science and Business Informatics. It offers a collaborative space for discussing and working through mathematical problems from courses such as Discrete Mathematics, Calculus, and Linear Algebra. Organized as a supervised study group, students can sign up, bring their questions, and form thematic groups to work on problems together. The focus is on peer learning, supported by guidance when needed.

Organization:

  • Please book an appointment in Stud.IP
  • First session: 

Description:

The Python Lab is a Master’s level course designed for students in Data Science and Artificial Intelligence in Molecular Sciences. The course provides students with the skills to design and implement small to medium software projects and analytical workflows, with a focus on statistics and machine learning. Students will learn how to work with real-world data in healthcare and biomedical fields, applying machine learning algorithms to analyze and interpret data. By the end of the course, students will be able to critically evaluate machine learning workflows and effectively use them to solve complex data science problems.

Organization:

  • entry via Stud.IP
  • Poster presentation and group project 
  • Kick-off meeting: 

Organization:

  • entry via Stud.IP
  • Kick-off meeting: 

Description:

This course equips students with the skills to clearly and effectively communicate scientific ideas in writing, presentations, and visualizations. Students will learn how to structure scientific texts, follow established guidelines, and practice writing a short review article. They will also explore how to design clear and engaging presentations through several short assignments, including a creative peer exercise. A further focus is placed on visualizations as an essential part of scientific communication: students will study principles of perception and design, learn how to map data to visual variables, and practice creating and critiquing effective figures.

The course is highly interactive and practice-oriented. Students work both individually and in groups, receive feedback on their work, and engage in peer discussions. By the end, participants will be able to write concise scientific texts, design meaningful visualizations, and deliver impactful presentations.

Organization:

  • Lecturers: Prof. Dr. Tim Kacprowski, Prof. Dr. Thomas Deserno, and Prof. Dr. Steffen Oeltze-Jafra

  • Classification: Bachelor and Master

  • Language: English

  • Regular Dates: Weekly

  • Entry via Stud.IP

  • First session: 

Available Thesis & Project Topics

Please contact t.kacprowski[a.t_))tu-braunschweig.de for an up-to-date list of available topics. 

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