Courses

In the Machine Learning track, you will develop deep technical expertise in core AI technologies, including deep learning, diffusion models, large language models, and scientific machine learning. You will also explore how these methods are applied in real-world domains such as the natural sciences, healthcare, and industry. Your Master’s thesis will give you the opportunity to contribute to the state of the art, either through theoretical or applied research, by working on academic projects with the department or in collaboration with our industrial partners. 

In the Neural Computing track, you will focus on the development of brain-inspired technologies that offer efficient, flexible alternatives to conventional AI. Key topics include: 

• Neuromorphic Systems – energy-efficient, brain-like architectures designed to match the flexibility and power efficiency of biological systems 
• Brain-Computer Interfaces – technologies that bridge biological and artificial systems, enabling new forms of human-computer interaction and clinical applications for brain disorders 

You’ll gain interdisciplinary knowledge spanning computational neuroscience, control theory, electrical engineering, and edge computing. In your thesis, you’ll apply this knowledge by developing your own neural-inspired systems — advancing both our understanding of the brain and the future of intelligent technology.