PhD Positions - Max Planck ETH Center for Learning Systems (CLS)

The PhD Positions at the Max Planck ETH Center for Learning Systems (CLS) involve a collaborative doctoral training program. The CLS is a joint initiative between ETH Zurich and several Max Planck Institutes, focusing on advanced research in learning systems. Fellows in this program are co-supervised by advisors from both ETH Zurich and one of the Max Planck Institutes or the ELLIS Institute Tübingen. The main day-to-day responsibilities include conducting research in areas such as AI for Science, AI Safety, Bio-inspired Robotics, and other related fields. Fellows will have a primary location based on their interests and will spend a mandatory 12-month exchange at the other location. All fellows register as graduate students at ETH Zurich and work towards completing their PhD project, which will culminate in a doctoral degree from ETH. To qualify for these positions, applicants must have a strong interest in basic research in relevant fields and hold or expect to hold an excellent Master's degree in a relevant subject. Proficiency in written and spoken English is essential. The program is fully funded, and the language of instruction is English. Nice-to-have skills include a strong interest in collaborative research and the ability to adapt to different research environments. The location offers unique opportunities for collaboration and exchange, with joint supervision by ETH Professors and MPI Directors or Group Leaders at the ELLIS Institute Tübingen. The program provides a supportive environment for young academics to engage in outstanding collaborations early in their careers. The position is based in Zurich and is a fixed-term, 100% commitment. What the role asks for: - A strong interest in fundamental research in areas such as AI for Science, AI Safety, Bio-inspired Robotics, Biomechanics, Causal Inference, Computational Biology, Computer Graphics, Computer Vision, Control Systems, Deep Learning, Digital Humans, Earth Observation, Educational Technology, Efficient AI, Explainable AI, Haptics, Human-Computer Interaction, Human-Robot Interaction, Imaging Technology, Machine Learning, Medical Informatics, Medical Robotics, Natural Language Processing, Neuroinformatics, Optimization, Physical AI, Probabilistic Models, Reinforcement Learning, Robotics, Security and Privacy, Smart Materials, Social Questions, Soft Robotics, Statistical Learning Theory, Visual Analytics. - An excellent degree or the expectation of obtaining an excellent degree at the Master level in a relevant field. - Good written and spoken English skills.