Neuroscientists empowered by Machine Learning

Research is changing. Neuroscience is swept by a new wave of innovation, led by Digitalization and Machine Learning / AI. Problems that used to hold up entire fields for decades, today see steady progress and incredible results. We are closer every day to fully understanding how the nervous system works, and the increasingly complex interfaces of human and machine. Working on this new state of the art however requires not only a top-notch formation in Neuroscience, but also a level of computational experience that was unthinkable just a decade ago.

Modern research, both in academia and industry, relies heavily in advanced computing and mathematical models. Neuroscientists with a training focused in the humanities aspect still need to work side by side with computational expert, often lacking a common glossary or understanding of each other's role. On the other hand, Neuroscience itself is such a vast and complex topic that it cannot be taught to mathematicians and computer scientists without a major investment.

The University of Fribourg fully acknowledges this new evolution of the field. The new Specialised Master in Digital Neuroscience creates a new professional figure spanning both the neuroscience and digital sides, by using a truly interdisciplinary approach. Students of our new program will receive a complete, impeccable training in Neuroscience, while complementing and extending it with a thorough training in Machine Learning and Data Analytics. You will be not just able to talk to other neuroscientists and computer scientists alike, but to practically replace them both: you will be able to design and execute experiments in a neuroscience lab, collect better data based on your expert assessment, then analyze it and model it yourself, in complete autonomy and with superior competence.

The number of seats available is limited: we are looking for the most promising students, capable of thinking out of the box and excel at radically different disciplines. You will not become a "jack of all trades, master of none": you will be fully trained on Neuroscience and Machine Learning alike, able to contribute to state-of-the-art research of both fields as a true expert. Our few, selected students will be surrounded by capable peers, and followed very closely by our teaching team.

If see yourself becoming part of our vision, go ahead and submit your application. We look forward to working with you.

Applications and career prospects

This Master's allows students to learn the methods and acquire the background to expertly engage in any neuroscience research project.

Academic work typically takes place in collaboration with experimentalists, neuroscientists experts in experiment design and implementation, who may have already acquired data that will benefit from the analytical expertise of our graduates.

Ideally a successful project provides novel solutions that could have not been obtained with traditional univariate methods. It is expected that such work can speed up also the academic career of PhDs to attract competitive funding and being considered for assistant professor positions very early on.

From an industry perspective, graduates are highly qualified to work in the sectors of healthcare (including public health), insurance, and the health and leisure industry. The training in machine learning methods, including the analysis of heterogeneous real-world data such as from wearable devices, smart phones and social media, provides rich opportunities in the domains of digital phenotyping and customised health applications.

It is expected that students spend minimal time in the laboratory. Rather it is expected that digital neuroscience students and graduates collaborate with colleagues from experimental sciences to provide quantitative analysis, guiding more advanced experiments or enable translation towards real world applications.

Student profile

We look for excellence, and prioritize merit and potential. Our ideal student resonates with the description of the study program, believes in this new professional figure, and is willing to undertake the hard path to achieve it. Neuroscience and Machine Learning are both among the toughest topics you can learn at a university level. Yet this program does not discount either: you preparation will approach that of someone taking two Master's in the related disciplines.

From a foundational perspective, this implies two separate set of prerequisites:

• Neuroscience: foundations of human medicine, nervous system, hands-on lab experience.
• Digitalization: foundations of informatics, math (specifically calculus, functional analysis, linear algebra and statistics), and software engineering

As it is unlikely for any Bachelor to check the complete list of requirement, most of these topics are taught as part of the program, with only math requiring possibly extra credits. What matters to us most is rather the mental agility necessary to study such different disciplines, the commitment and passion to round both sides of the training, and an overall drive for excellence.

Meet the team

Michael C Schmid is a Professor of Systems Neuroscience. His laboratory uses an interdisciplinary approach to investigate vision in health and disease. Current projects focus on delineating brain plasticity and developing novel cortical prosthesis approaches in blindness. A second line of research is aimed at characterizing and improving reading deficits in developmental dyslexia.

Denis Lalanne is a Professor of Informatics and director of the Human-IST institute. His focus is on the topic of Human-Machine Interaction, specifically multimodal interaction and information visualization. Within the smart living lab (.ch), his research focuses on human-building interactions and technologies to understand and improve humans' comfort and interactions within the built environment. Together with the creation of the Swiss Center on Augmented Intelligence SCAI, his research interests moved towards Human-centered and interactive Artificial Intelligence.

Roberto Caldara is a Professor of Psychology with an original interdisciplinary profile in Visual and Social Neuroscience. He is interested on how visual information is processed and integrated by humans, from micro eye movements into macro neural processes. His expertise includes behavioral, computational modeling, electrophysiology (EEG), functional Magnetic Resonance Imaging (fMRI), brain damaged patients and eye movement approaches with adults, and more recently studies with healthy and clinically diagnosed children.

Björn Rasch is a Professor of Psychology with a focus in cognitive neuroscience and sleep. His main interest is the interaction between cognitive processes (our thoughts, imaginations, memories) and our biological functioning, especially during states of reduced consciousness like sleep. He research interest includes the acquisition, analysis and interpretation of sleep and health-related data outside of the laboratory.

Gregor Rainer is a Professor in Systems Neuroscience.  He studies brain mechanisms of neuromodulation and how they contribute to sensory information processing and brain state regulation.  He uses a comparative approach studying a variety of mammalian species, and employs computational methods for analysis and interpretation of neural and behavioural data.

Marcus Haag is the Study Coordinator and Deputy Head of the Medical Studies Department at the University of Bern, Switzerland, and Study Advisor and Program Coordinator for the Master in Digital Neuroscience at the University of Freiburg. He has dedicated his carreer to understanding neuronal interactions and their implications, from neuronal networks to disease pathology. He has a Ph.D. in Neuropharmacology from the University of Reading and extensive postdoctoral experience at leading institutions, including Newcastle University and the University of Freiburg.

What is in the Study Plan

The Study Plan is the official contract of the Master's program. There you will find all requirements and procedures necessary to obtain your degree, such as a list of all required work, mandatory and eligible courses with extensive descriptions, and a primer on the thesis work. You can download it here [link] (currently approved draft, official release in July 2022).