Apply until November 5, 2025 for a PhD in CLS

In our highly competitive CLS doctoral training program, fellows are co-supervised by one advisor from ETH Zurich and one from the MPI for Intelligent Systems in Stuttgart / Tübingen, the MPI for Informatics in Saarbrücken or the ELLIS Institute Tübingen. Each CLS fellow has a primary location (chosen based on interests and match) and conducts a mandatory 12-month exchange at the other location. All CLS Ph.D. fellows register as graduate students at ETH Zurich and, upon successful completion of their Ph.D. project, will be granted a doctoral degree by ETH. Fellowships are fully funded. The program language is English.

Faculty participating in this call (only available as co-advisor for the one-year exchange):  Zurich: Andreas Krause, Benjamin Grewe, Christian Holz, Daniel Razansky, Fanny Yang, Florian Dörfler, Gunnar Rätsch, Hedan Bai, Lars Lindemann, Marc Pollefeys, Menna El-Assady, Mrinmaya Sachan, Niao He, Richard Hahnloser, Robert Katzschmann, Ryan Cotterell, Siddhartha Mishra, Siyu Tang, Stelian Coros, Thomas Hofmann, Valentina Boeva*. Stuttgart/ Saarbrücken/ Tübingen: Antonia Georgopoulou, Antonio Orvieto*, Bernhard Schölkopf, Bernt Schiele, Buse Aktaş, Celestine Mendler-Dünner, Christian Theobalt, Christoph Keplinger, Jonas Geiping*, Katherine J. Kuchenbecker, Konstantin Rusch, Maksym Andriushchenko, Maximilian Dax, Michael Mühlebach, Moritz Hardt, Philipp Müller, Rediet Abebe, Renate Sachse, Sahar Abdelnabi, Shiwei Liu, Wieland Brendel.*

Applicants should have a strong interest in doing basic research in areas such as: Bio-inspired /Bio-hybrid Robotics, Biomechanics, Causal Inference, Computational Biology, Computer Graphics, Computer Vision, Control Systems, Deep Learning, Digital Humans, Earth Observation, Educational Technology, Efficient AI, Explainable AI, Graph Representation Learning, Haptics, Human-Computer Interaction, Human-Robot Interaction, Imaging Technology, Machine Learning, Medical Informatics, Medical Robotics, Natural Language Processing, Neuroinformatics, Optimization, Perceptual Inference, Probabilistic Models, Reinforcement Learning, Robotics, Safety, Security and Privacy, Scientific Machine Learning, Smart Materials, Social Questions, Soft Robotics, Statistical Learning Theory, Visual Analytics.