Life Science Informatics (b-it)

Overview The programme is an international, English-taught Master’s that combines life sciences and computer science, emphasizing both theoretical foundations and practical IT skills. Teaching is tightly linked with research: many lab courses are conducted within active research projects at the participating Fraunhofer institutes (Applied Information Technology — FIT, and Algorithms and Scientific Computing — SCAI) and are carried out in cooperation with regional industry partners. This close research-teaching integration prepares students to work at the interface of biology and informatics.

Structure and core content The curriculum is organised into four main blocks: computer science and mathematics tailored for natural scientists; core principles of life science informatics; cell biology and systems biology; and biomedical data science & artificial intelligence. Coursework and practicals cover a wide range of topics, including biomedical database systems, information management and retrieval, data mining and machine learning, statistical genetics, drug design, medical imaging and visualization, computational neuroscience, modelling of regulatory and metabolic networks, systems and computational systems biology, microarray analysis, and biomedical data science/AI.

Practical focus, ethics and thesis Hands-on learning is emphasised through research lab courses, internships, and specialised modules on information retrieval, data mining, data science and AI, with a strong application focus on biomedical information systems and biomedical data science. The programme also includes dedicated training on the ethical implications of new biotechnologies. The final six months are reserved for the Master’s thesis, which can be carried out in collaboration with academic or industry partners.

Program at a glance

• Language of instruction: English
• Strong research integration via Fraunhofer FIT and SCAI and collaboration with regional industry
• Four curricular blocks: CS & mathematics for natural scientists; life science informatics fundamentals; cell biology & systems biology; biomedical data science & AI
• Practical components: research lab courses and internships embedded in basic and applied research projects
• Thematic strengths: databases, information retrieval, data mining/ML, statistical genetics, drug design, medical imaging, computational neuroscience, network modelling, systems biology, microarray analysis, biomedical AI
• Ethics training on emerging biotechnologies included
• Final six months dedicated to the Master’s thesis (industry collaboration possible)

Overview The programme follows the ECTS framework and combines taught courses, hands-on laboratory work and research. Study unfolds over three semesters of lectures and lab courses, followed by a fourth semester dedicated to the Master's thesis. Most lectures include accompanying assignments that must be successfully completed, and laboratory courses form a substantial, practical component of the curriculum. The course mix includes several compulsory modules in early semesters and a wide range of electives in later terms, allowing you to tailor your learning path.

Research exposure and professional connections A key feature is early and sustained contact with leading researchers: guest lectures and special lecture series bring internationally prominent scientists into the programme. In particular, the LSI International Lecture Series (offered each summer semester) gives students the chance to engage with current topics and discuss them directly with experienced researchers. Company visits and targeted workshops further supplement academic teaching by offering insight into industrial research and practical applications.

Program requirements (concise)

• Complete three semesters of lectures and laboratory coursework under the ECTS system
• Successfully finish course assignments associated with lectures
• Participate in and complete substantial lab courses
• Fulfil mandatory core modules and selected elective modules as required by the programme
• Write and submit a Master’s thesis in the fourth semester

Key modules and learning outcomes

• Core modules and lab courses: build foundational knowledge in life science informatics and develop practical, hands-on laboratory skills
• Elective modules: enable specialization and breadth in advanced topics relevant to your interests
• International Lecture Series and guest lectures: broaden scientific perspectives and improve ability to critically discuss current research
• Workshops and company visits: provide context on industrial research practices and career-relevant skills
• Master’s thesis: demonstrate capacity for independent research, project design, data analysis and scientific communication appropriate for academic or industry roles

Graduates are prepared for research and development roles at the intersection of biology and informatics, such as computational biologist, bioinformatics scientist, biomedical data scientist, or machine learning engineer in biotech, pharmaceutical companies, medical imaging firms and healthcare IT. The programme's emphasis on lab work, data science/AI, and industry cooperation facilitates direct entry into applied R&D and product development roles.

The strong research orientation and exposure to academic guest researchers also make graduates competitive for PhD programmes and positions at research institutes (including Fraunhofer institutes and university labs). Career paths may include roles in systems biology, statistical genetics, drug design, computational neuroscience, and related fields in both industry and academia.