Neural Engineering (MSc)

Neural Engineering brings together neuroscience and engineering to decode, interact with, and influence the nervous system. The programme covers a range of applications — from brain–computer interfaces and active neuroimplants to neuroergonomic human–machine systems, neurocognitive and affective modelling, and AI systems inspired by neural principles. Its interdisciplinary approach equips students to design solutions that bridge biological understanding and engineering implementation.

Graduates are prepared for technically demanding R&D roles in industry, for example in the design and development of nerve stimulators, diagnostic devices, and active implants. Emerging employment areas include neuroergonomics (notably in the automotive sector), neurocybernetics for collaborative robotics, and bespoke neurorehabilitation technologies. A substantial portion of alumni also choose to continue into doctoral studies; the programme’s national and international partnerships give strong infrastructure and support for PhD preparation.

Students take part in international research projects, gaining early, active engagement in the scientific community and hands-on experience. Through these collaborations they develop the scientific knowledge, practical skills and interpersonal abilities that are important for leadership roles in academia or industry. The programme’s international orientation also helps students build global networks and confidence for careers on an international stage.

Requirements / programme expectations

• Active participation in international research projects with national and international partners
• Early engagement in the scientific community through hands-on research activities
• Development of both scientific knowledge and social/leadership skills for advanced roles
• Preparation and pathways available for continuation to doctoral studies
• Readiness to work in applied R&D fields such as implantable devices, diagnostics, neuroergonomics, neurocybernetics, and neurorehabilitation

Overview

This MSc curriculum builds a foundation in neural and cognitive systems before moving into advanced analysis, modelling and interface technologies. In the first semester you study core topics such as auditory processing and perception, functional neuroimaging, biomedical signal and image processing, and the practical design and manufacture of active implants. You also learn essentials of risk management and biocompatibility specifically geared toward implantable systems.

In the second semester the focus shifts to neural signal analysis and mathematical models of neuronal activity, technical interfaces to the nervous system, neuro-inspired artificial intelligence, and clinical neurophysiology. Compulsory coursework is complemented by elective modules, and a strong emphasis is placed on hands-on laboratory work to consolidate concepts and develop practical skills. Student projects and theses may be completed in the programme’s labs or with external partners (for example, Bosenberg Clinics, Fraunhofer Institute for Biomedical Engineering, Saarland University Hospital), and internships with partner organisations abroad are possible to gain real-world research or clinical experience. Teaching is supported by close supervision from professors and staff and supplemented by guest lecturers when available.

The third semester is dedicated to the Master’s thesis, which may also be carried out externally. Thesis results are presented in a seminar, and students are encouraged to publish and present their findings; historically many theses have led to journal articles or conference presentations.

Key modules (examples)

• Auditory processing and perception
• Functional imaging methods in neuroscience
• Biomedical signal and image processing
• Practical manufacture of active implants (including biocompatibility and risk management)
• Neural signal analysis and mathematical modelling of neuronal activity
• Technical interfaces to the nervous system
• Neuro-inspired artificial intelligence
• Clinical neurophysiology
• Elective modules and hands-on lab courses

Typical learning outcomes

• Understand neural and cognitive system principles, including sensory processing and neuroimaging techniques
• Analyse and model neuronal signals and activity using mathematical and computational tools
• Design and evaluate technical interfaces to the nervous system, with consideration of safety, risk and biocompatibility for implantable devices
• Apply biomedical signal and image processing methods in experimental and clinical contexts
• Develop practical skills through laboratory work, device fabrication and project-based research
• Conduct an independent research project, present results, and prepare work suitable for publication or conference presentation

Programme requirements (concise)

• Complete the taught modules across the first and second semesters, including compulsory practical components
• Choose and complete elective modules as offered by the programme
• Undertake hands-on projects and laboratory work to demonstrate practical competence
• Submit and defend a Master’s thesis in the third semester (external thesis option permitted) and present results in a seminar
• Optional: pursue internships or external collaborations with partner institutes or clinics to gain applied experience

Graduates are prepared for demanding R&D roles in industry—developing nerve stimulators, diagnostic devices, active implants and other neurotechnology products. Additional application areas include neuroergonomics (for example in the automotive sector), neurocybernetics for collaborative robotics, and customised solutions in neurorehabilitation.

A significant share of alumni pursue academic careers and doctoral programmes; the programme’s research partnerships and publication-oriented thesis work provide a solid foundation for continuing in research. Practical internships and project collaborations also help graduates transition directly into industry roles.