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.
Courses by Semester
Winter Semester 25/26
Python Lab (PyLab)
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
Bioinformatik and Biostatistik I (BIBS I)
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
Seminar Data Science in Biomedicine
Organization:
- entry via Stud.IP (BA), Stud.IP (MA)
- Kick-off meeting: 23.10.2025 @ 15:00 in R107/108 BRICS
Better Scientific Presentation and Writing (BSPW)
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
Lerntreff Mathe
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
Teamprojekt
Summer Semester 25
Network Biology (NetBio)
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:
Python Lab (PyLab)
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:
Scientific and Method-Oriented Writing (SciMOW)
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:
Bioinformatik and Biostatistik II (BIBS II)
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:
Python for Life Scientists (STEMPy)
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:
Seminar Data Science in Biomedicine
Organization:
- entry via Stud.IP
- Kick-off meeting:
Lerntreff Mathe
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:
Teamprojekt
Winter Semester 24/25
Python Lab (PyLab)
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:
Seminar Data Science in Biomedicine
Organization:
- entry via Stud.IP
- Kick-off meeting:
Better Scientific Presentation and Writing (BSPW)
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:
Teamprojekt
Available Thesis & Project Topics
Please contact t.kacprowski[a.t_))tu-braunschweig.de for an up-to-date list of available topics.