study programme	degree	semester	ASPO	faculty	institutes	abbreviation	abbreviation (SAP)
Neural Engineering	M.Sc.	3	2025			NEM	MA_NE
prog acc	system acc	acc cluster
28.09.2018-30.09.2023	30.09.2025	IngWi-I

Handbook [PDF] (Dec 4 09:46:33 2024)

[display qualification goals] ID short description qualification goal last change Q1 Knowledge and Understanding Graduates have built on the skills and competencies acquired through an initial professionally qualifying higher education degree in the fields of neurotechnology and neuroscience. They possess detailed, in-depth knowledge of the interfaces between the human nervous system, as well as technical applications and software solutions for recording and processing psychophysiological data. They will also be able to expand this knowledge independently and, building on this foundation, identify problems and develop, implement and test technical solutions in accordance with scientific standards in diagnostics, for the control of human-machine interfaces and for electrically active implants. 15.06.2026 HQR-Bezug Qualifikationsziel Q1 Wissen und Verstehen Einsatz, Erzeugung und Anwendung von Wissen Kommunikation und Kooperation wissenschaftliches Selbstverständnis / Professionalität X Q2 Communication and cooperation, professionalism: Graduates are able to carry out project management tasks and contribute constructively to teamwork. They are able to work in interdisciplinary and international teams, particularly with medical professionals, and to communicate with them in a manner appropriate to the field. They can convincingly present and explain their work results to both expert and non-expert audiences. 15.06.2026 HQR-Bezug Qualifikationsziel Q2 Wissen und Verstehen Einsatz, Erzeugung und Anwendung von Wissen Kommunikation und Kooperation wissenschaftliches Selbstverständnis / Professionalität X Q3 Application and generation of knowledge Graduates are able to address medical, neurotechnological and neuroscientific problems using appropriate methodologies and in accordance with the standards of good scientific practice. They are familiar with the current state of the literature in their field as well as selected research questions. They will be able to independently expand their specialist knowledge in the field of neural engineering. Furthermore, the graduates will be able to apply appropriate scientific methods to develop and optimise products and processes in neural engineering and biomedical technology. 15.06.2026 HQR-Bezug Qualifikationsziel Q3 Wissen und Verstehen Einsatz, Erzeugung und Anwendung von Wissen Kommunikation und Kooperation wissenschaftliches Selbstverständnis / Professionalität X Q4 Academic self-awareness, professionalism: Graduates are able to take into account the business, regulatory, clinical and ethical implications of medical and neurotechnological solutions. They are able to reflect on their social responsibility when implementing medical and neurotechnological solutions. 15.06.2026 HQR-Bezug Qualifikationsziel Q4 Wissen und Verstehen Einsatz, Erzeugung und Anwendung von Wissen Kommunikation und Kooperation wissenschaftliches Selbstverständnis / Professionalität X

[display learning outcomes] ID Lernergebnis Module L1 The ability to describe and model biological and technical systems mathematically L2 Methods of signal acquisition and processing in the fields of psychophysiology, clinical neurophysiology and audiology. L3 Understanding of how technical components and systems operate at the bio-technical interface and in human-machine interfaces. Knowledge of neural and cognitive-affective parameters in brain-computer interfaces, neuroprosthetics and neurostimulation L4 Ability to design appropriate technical solutions, taking into account the specific aspects of the interaction between technical systems and the human body L5 The specific risks and safety requirements of medical technology, with a focus on implantable systems L6 Fundamental principles and mechanisms of machine learning and neuro-inspired artificial intelligence (e.g. neural networks and common machine learning methods) and their applicability to practical problems. Understanding of the similarities and differences between mathematical neuron models, modern AI techniques and machine learning L7 Understanding of physiological processes and medical-pathological issues L8 Central and autonomous correlates of cognitive-affective processing, with a particular focus on neuroergonomics L9 The ability to independently source and evaluate information. Knowledge of specific sources of information in medical research. L10 The application of the methods learnt in technical and scientific practice L11 The ability to communicate with medical professionals, ability to give interdisciplinary presentations L12 The ability to work in a team, key social and intercultural skills L13 Project and planning skills, the ability to make independent decisions whilst taking into account the technical, medical, economic and social context and possibilities.

[course type distribution]

Mandatory Courses

Optional Courses

Summary ECTS per semester (mandatory courses)

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