Two Postdoctoral Positions in Machine Learning for Alzheimer's Disease

100%, Basel, temporary

The Machine Learning and Computational Biology Laboratory at the Department of Biosystems Science and Engineering (D-BSSE) is seeking two postdoctoral researchers to work on Machine Learning for Alzheimer's Disease. The postdocs will be joining a highly interdisciplinary research team and will be supervised by Dr. Catherine Jutzeler/Dr. Bastian Rieck and Prof. Karsten Borgwardt. The positions should start no later than February 15, 2020.

The Department of Biosystems Science and Engineering (D-BSSE) is one of ETH Zurich's youngest departments and the only one located in Basel outside of the Zurich campus. It unites biologists, engineers, computer scientists, and mathematicians to work towards a quantitative understanding and purposeful engineering of complex biological systems.

Job description

The positions are funded by Dr. Catherine Jutzeler's and Dr. Bastian Rieck's recently awarded SNSF Spark Grants on Machine Learning for Alzheimer's Disease.

Alzheimer’s Disease (AD) is the sixth-leading cause of death for Americans ages 65 years and older. It is an irreversible, progressive brain disorder that slowly destroys memory and, eventually, an individual’s ability to perform even the simplest tasks, such as bathing, feeding, and dressing. Beyond the immediate health consequences, the societal costs are of epidemic proportion, thus making AD a pressing public health and medical problem. With disease-modifying treatment trials still unsuccessful at the present time and only medications to treat symptoms available, an emerging research initiative is to identify approaches to intervene before the damage begins, making it potentially possible to prevent AD.

The two positions will focus on either one of the following two promising approaches:

  • Analyzing changes in brain connectivity over time by developing novel techniques based on topological data analysis (TDA), a recent paradigm for multivariate data analysis, and integrating them into modern machine learning models.
  • Developing a “surrogate human disease model” based on knowledge about traumatic brain injury, a central nervous disease with an acute onset whose pathologies closely resemble those of Alzheimer’s disease. Using techniques from the domain of transfer learning, knowledge about traumatic brain injuries should then be translated to Alzheimer’s disease.

Your profile

Applicants should be highly motivated and creative, show an exceptional track record, and hold a doctoral degree in Computer Science, Bioinformatics, Engineering, Mathematics, Statistics, or related fields, and be interested in working in an interdisciplinary environment at the interface of Machine Learning and Personalized Medicine. Experience in developing algorithms for large-scale data analysis problems in biology and medicine is an asset. Academic excellence, a professional work attitude, and a proactive and self-driven work ethic are expected. The positions offer the opportunity to gain leadership and supervision experience in joint projects with younger scientists. Initially, the positions are fully funded for one year. They will be located in the Machine Learning and Computational Biology Laboratory at the Department of Biosystems Science and Engineering at the ETH Campus in Basel.

ETH Zurich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
Working, teaching and research at ETH Zurich Link icon


We look forward to receiving your online application with the following documents:

  • CV including publication list, and 
  • a letter of motivation (up to 2 pages long), in which you describe your motivation to join the lab and in which you explain which publications of the lab you are most interested in.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about the Machine Learning and Computational Biology lab can be found on our website For questions about the positions, please contact (no applications).

Your workplace