Computational Neuroscience

This master's program trains students to combine quantitative methods with experimental neuroscience. Courses cover analysis and modeling of neural data, the physical and physiological principles behind neural recordings and brain imaging, and computational approaches to motor control and vision. You will also study systems neuroscience and neurophysiology, the basic mechanisms of learning and memory, and theoretical approaches to behaviour and cognition. Mathematical and statistical foundations, programming, and machine learning specifically applied to neuroscience are integrated throughout the curriculum.

Hands-on and applied work is an explicit part of the degree: students can carry out an essay rotation and a laboratory rotation in research groups they choose, and the Master’s thesis is likewise completed in a lab selected by the student. The program therefore balances theory and methods with practical experience in experimental or computational labs, including topics such as rehabilitation robotics and brain–computer interfaces.

For international students, the program is taught in English and is suited to those wanting to work at the interface of biology, computation and engineering—either in research settings or in technology-driven roles that rely on neural data analysis and modelling.

Program components and requirements (high-level)

• Core coursework: neural data analysis; models of neural coding and computation; theoretical neuroscience; behaviour and cognition
• Methods and foundations: mathematics, statistics, programming, machine learning for neuroscience
• Applied topics: physical/physiological bases of neural recordings and brain imaging; computational motor control and vision; rehabilitation robotics and brain–computer interfaces
• Practical training: essay rotation and laboratory rotation in student-chosen labs
• Research requirement: Master’s thesis completed in a lab selected by the student
• Language of instruction: English

Overview

This two-year (four-semester) Master’s program combines intensive classroom training with extended hands-on research. During the first two semesters you take theory-focused courses delivered as lectures, seminars, tutorials and journal clubs, with formal examination periods at the end of each semester. These activities are designed to build a rigorous conceptual foundation in computational neuroscience and to develop skills in critical reading, scientific communication and problem solving.

Research training and outcomes

The second year is devoted to practical research: a seven-week essay rotation, a ten-week laboratory rotation, and a six-month Master’s thesis. These rotations give you short-term project experience and exposure to different research environments, while the thesis provides an opportunity to design and execute an independent research project. Graduates will leave with a blend of theoretical knowledge, experience interpreting primary literature, hands-on experimental or computational skills, and the ability to carry out an extended research project — preparation that commonly enables direct transition into PhD studies.

Key program elements and learning outcomes

• Rigorous theoretical coursework through lectures, seminars, tutorials and journal clubs
• Formal examination periods at the end of semesters 1 and 2
• Seven-week essay rotation to practice focused literature- or analysis-based projects
• Ten-week lab rotation to gain practical, hands-on research experience
• Six-month Master’s thesis involving independent research and scientific writing
• Graduates acquire the ability to critically evaluate neuroscience research, implement computational or experimental methods, and lead a research project; the thesis is frequently positioned to feed directly into PhD work.

Graduates are well positioned for research careers in academia and industry, including doctoral studies (many theses feed directly into PhD programmes), roles in neurotechnology and brain–computer interface development, and positions that apply data science and machine learning to neural data. The combination of computational skills and hands-on laboratory experience also suits careers in medical technology, rehabilitation robotics, neuroimaging analysis, and biotech companies focusing on neural systems.

Alumni can expect to work in interdisciplinary teams at research institutes, universities, clinical research centres, and technology firms, or to continue into specialised doctoral training in computational or experimental neuroscience.