Neural & Behavioural Sciences

Program overview

This English-language master's curriculum combines foundational and advanced topics across experimental and computational neuroscience. Teaching covers functional neuroanatomy, sensory and motor systems, statistics and mathematics, theoretical and computational neuroscience, Matlab programming, neurophysiology, experimental methods, behaviour and cognition, sensorimotor integration, neuropsychology and functional brain imaging. The course list is designed to give both conceptual background and practical skills used in contemporary brain research.

Practical training and research

Students complete two laboratory rotations followed by a research-focused Master's thesis; all three can be carried out in laboratories chosen by the student, allowing you to tailor your training to your interests and to gain hands-on experience before committing to a thesis project. Detailed course descriptions and up-to-date syllabi are available on the graduate school’s website, which you should consult when planning your studies.

Key features

• Lectures spanning anatomy, systems neuroscience, cognitive and behavioural topics, and neuroimaging
• Training in statistics, mathematics, and Matlab programming for data analysis and modelling
• Courses on neurophysiology and experimental methods used in neuroscience research
• Two laboratory rotations plus an independent Master's thesis, each selectable within available university labs
• Detailed module descriptions provided on the graduate school website for course planning and credit information

Program structure — what to expect

This Master's runs over four semesters. The first two semesters focus on classroom-based learning: lectures, seminars, tutorials and journal clubs form the core teaching formats, and there is an examination period at the end of each semester. These modules build a solid theoretical foundation in neural and behavioural sciences and train you to read, discuss and present primary literature.

The third semester is dedicated to practical training through two laboratory rotations, each lasting ten weeks. These rotations give you hands-on experience with experimental techniques, data collection and analysis while exposing you to different research environments and methodologies. The final semester is reserved for the six-month Master’s thesis, an independent research project in which you apply the knowledge and skills acquired earlier to produce a substantial piece of original work.

Key modules and learning outcomes

• Core teaching formats: lectures, seminars, tutorials and journal clubs — emphasizing theoretical grounding, critical reading and scientific discussion.
• Practical modules: two lab rotations (10 weeks each) — focused on experimental technique, troubleshooting, and working within active research groups.
• Capstone: six-month Master’s thesis — independent project work, experimental planning, data analysis and scientific reporting.
• Learning outcomes include advanced conceptual understanding of neural and behavioural sciences, ability to critically evaluate scientific literature, practical laboratory competence, experience in experimental design and data interpretation, and the capacity to carry out an independent research project suitable for further research training or research-oriented careers.

Program requirements (curriculum milestones)

• Total duration: 4 semesters
• Semesters 1–2: theoretical coursework (lectures, seminars, tutorials, journal clubs) with end-of-semester examinations
• Semester 3: two laboratory rotations, each 10 weeks long
• Semester 4: six-month Master’s thesis (independent research and thesis write-up)

Graduates are prepared for research-focused careers in academia and research institutions, including progression to PhD programmes (the programme explicitly supports combined Master’s-to-PhD pathways). The mix of experimental and computational training makes graduates competitive for positions in neuroscience laboratories, neurotechnology and research groups studying cognition and brain function.

Transferable skills such as data analysis, programming (e.g. Matlab), experimental design and scientific communication also open opportunities in industry settings (biotechnology, medical device and software companies), science policy, and research support roles.