International Master's Programme in Computational Neuroscience

Overview

This English-taught, interdisciplinary master's program trains students to use mathematical, physical, computational and engineering methods to understand brain function. It combines experimental work, data analysis and theoretical modelling to build a common scientific language across neurobiology, cognitive science and information technology. Graduates are positioned to contribute to fundamental research, improved prevention and treatment of neural disorders, advances in information technology and human–machine interaction, and more effective strategies for teaching and learning.

Structure and learning approach

The curriculum is divided into a foundation phase and a research-focused phase. In the first year students reach a high level of competence in core topics through coursework that covers theoretical and experimental perspectives, machine learning and programming, plus tailored individual study. The second year emphasizes hands-on research: students complete three lab rotations (including at least one theoretical and one experimental project), take courses on ethics and advanced topics, and write a research-based Master’s thesis that is concluded with an oral defence.

Practical details and location

The program is organised by the Bernstein Center for Computational Neuroscience (BCCN Berlin) as a joint degree of the two Berlin universities; teaching takes place at HU Berlin (BCCN Berlin, Campus Nord, postal code 10115) and at TU Berlin (postal code 10587). Admitted students are offered preparatory courses in mathematics and neurobiology in September–October before the winter semester begins.

Core modules and credit points (CP)

• Models of Neural Systems — 12 CP
• Models of Higher Brain Functions — 12 CP
• Acquisition and Analysis of Neural Data — 12 CP
• Machine Intelligence (machine learning and neural networks) — 12 CP
• Programming Course and Project — 6 CP
• Individual, mentor-guided studies to close knowledge gaps — 6 CP
• Lab rotations: three research projects (approx. two months each; at least one theoretical and one experimental) — 3 × 9 CP
• Ethical issues and societal implications of brain research (obligatory) — 3 CP
• Elective advanced-topic courses (third and fourth semesters) — 10 CP (third sem.) + 10 CP (fourth sem.)
• Master’s thesis research — 20 CP (includes oral presentation/defence)

If you would like, I can help summarize admission prerequisites, application deadlines, or typical backgrounds that successful applicants have.

Curriculum overview

This 120‑credit interdisciplinary Master’s combines theory, computation and hands‑on lab work to train students in contemporary approaches to brain modelling and data analysis. Teaching mixes lectures, tutorials (analytical and mathematical problem solving, programming exercises), laboratory practicals, project work (programming projects) and seminars so that different methods within a module reinforce each other. Most modules are assessed by an oral exam, and the curriculum balances individual study with collaborative projects to prepare you for research or industry roles that require quantitative, experimental and ethical awareness.

Key modules and learning outcomes

• Models of Neural Systems: builds essential neurobiological knowledge and theoretical modelling approaches. You will learn to select appropriate theoretical methods for a given neural system and apply them to simulate and interpret neural processes.
• Models of Higher Brain Functions: focuses on modelling cognitive functions (vision, memory, attention, executive functions) grounded in neurobiology and psychophysics, giving you tools to formalize and test hypotheses about higher‑order brain processes.
• Acquisition and Analysis of Neural Data: covers major experimental techniques for collecting neural signals and the analytical methods used to process them. You will understand application domains, strengths and limitations of methods, and gain familiarity with raw experimental data.
• Machine Intelligence: introduces core artificial intelligence and machine learning methods, teaching you how to evaluate algorithmic performance and apply these techniques to neuroscience and related application areas.
• Programming Course and Project: develops both basic and advanced programming skills; you will produce complex software, and as part of a team create a larger program with proper specifications, documentation and testing.
• Individual Studies: offers the chance to close personal gaps in mathematics, programming or neurobiology so you can fully engage with the interdisciplinary curriculum.
• Ethical Issues and Implications for Society: fosters critical reflection on ethical and societal consequences of modern neuroscience research and applications.
• Courses on Advanced Topics: lets you deepen expertise in specific areas of computational neuroscience according to your interests.
• Lab Rotation: provides short research placements where you tackle a scientific question together with a host research group and present your findings—excellent preparation for thesis work or doctoral research.

Additional notes

Elective advanced courses are available to shape the program to your goals. Admitted students may attend preparatory courses in mathematics and neurobiology held from September to October before the official start. A fast‑track option is offered via the Einstein Center for Neurosciences Berlin—please contact BCCN Berlin for details.

Program structure — key requirements (concise)

• Total workload: 120 credit points (ECTS)
• Most modules culminate in an oral exam
• Mixed teaching formats: lectures, tutorials, practicals, projects, seminars
• Mandatory practical experience via lab rotation and programming projects
• Option to take elective advanced topics and individual studies to fill gaps
• Preparatory courses available (Sept–Oct) for admitted students
• Fast‑track pathway available through the Einstein Center for Neurosciences Berlin (contact BCCN Berlin)

Graduates are equipped for research careers in academia and industry where quantitative modelling, data analysis and machine-learning skills are in demand. Typical pathways include doctoral research in neuroscience, positions in neurotechnology and brain–computer interface companies, roles in AI and machine-learning teams, and R&D in medical research, biomedical engineering or cognitive computing.

The programme’s blend of experimental methods and computational skills also prepares students for data-science roles in biotech and healthcare companies, as well as for interdisciplinary work on human–machine interaction, neural signal processing and translational projects relating to diagnosis and treatment of neural disorders.