In our fully-funded doctoral fellowship program, Ph.D. students are co-supervised by one advisor from ETH Zurich and one from the Max Planck Society, primarily the Max Planck Institute for Intelligent Systems (Stuttgart / Tuebingen, Germany). All doctoral fellows will register as graduate students at ETH Zurich and, upon successful completion of their Ph.D. project, will be granted a doctoral degree by ETH.
Applicants must hold or expect to obtain an excellent degree at Masters level in a relevant subject and should have a strong interest in basic research in areas such as:
Bio-inspired / Bio-hybrid Robotics, Causal Inference, Computational Biology, Computer Graphics, Computer Vision, Control Systems, Digital Humans, Earth Observation, Haptics, Human-Computer Interaction, Human-Robot Interaction, Imaging Technology, Machine Learning, Medical Informatics, Micro- and Nano-Robotics, Natural Language Processing , Neuroinformatics, Optimization, Perceptual Inference, Probabilistic Models, Reinforcement Learning, Robotics, Social Questions, Soft Robotics, Statistical Learning Theory.
Participating faculty include:
Afonso Bandeira, Andreas Krause, Benjamin Grewe, Bernhard Schölkopf, Christian Holz, Christoph Keplinger, Daniel Razansky, David Steurer, Fanny Yang, Fisher Yu, Florian Tramèr, Florian Dörfler, Gunnar Rätsch, Jörg Stückler, Justus Thies, Katherine Kuchenbecker, Klaas Prüssmann, Konrad Schindler, Marc Pollefeys, Marco Hutter, Mehmet Fatih Yanik, Metin Sitti, Michael Black, Michael Mühlebach, Moritz Hardt, Mrinmaya Sachan, Niao He, Otmar Hilliges, Richard Hahnloser, Robert Katzschmann, Samira Samadi, Siyu Tang, Thomas Hofmann, Wieland Brendel.
Watch our video about doing a Ph.D. degree within the Max Planck ETH Center for Learning Systems.
We value diversity at our institutions. Anyone – of any gender identity, nationality or background - who meets our academic requirements is encouraged to apply. We seek to increase the number of women in areas where they are underrepresented, so we explicitly encourage women to apply.
Apply online to join in 2023. Deadline for the application: November 15, 2022.