PhD Fellowships at the Max Planck ETH Center for Learning Systems in Switzerland | ETH Zürich

The Max Planck ETH Center for Learning Systems is a joint research center of ETH Zurich and the Max Planck Society. The Center’s mission is to pursue research in the design and analysis of learning systems, synthetic or natural. This initiative brings together more than 30 professors and senior researchers in the fields of machine learning, perception, robotics on large and small scales, as well as neuroscience.

PhD Fellowships at the Max Planck ETH Center for Learning Systems

The Center offers a unique fellowship program, where PhD students are co-supervised by one advisor from ETH Zurich and one from the MPI for Intelligent Systems in Tübingen and Stuttgart. PhD students are expected to take advantage of the opportunities offered by both organizations and to actively seek cross-group collaborations. The Center also offers a wide range of activities like retreats, workshops, and summer schools. This is an exciting new program and we expect admission to be highly competitive. Each PhD fellow will have a primary location (chosen based on interests and match) and is expected to spend about one year at the other site as well. Fellowships will be remunerated through employment contracts, subjected to the rules of the Max-Planck-Society and ETH Zurich, respectively. All PhD fellows will register as graduate students at ETH Zurich and – upon successful completion of their PhD project – be granted a doctoral degree by ETH Zurich. Details of this process are governed by ETH regulations and committees. The deadline for applications is January 10, 2016. The selection interviews will take place on February 15 and February 16 at the Max Planck Society’s Ringberg Castle, Tegernsee, Bavaria.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering, Materials Science, Neuroscience and related fields, and a keen interest in doing basic research in areas like: Machine Learning and Empirical Inference of Complex Systems, Machine Intelligence, including Machine Vision and Natural Language Understanding, Perception-Action-Cycle for Autonomous Systems, Robust Model-Based Control for Intelligent Behavior, Robust Perception in Complex Environments, Design, Fabrication, and Control of Synthetic, Bio-Inspired, and Bio-Hybrid Micro/Nanoscale Robotic Systems, or Neurotechnology and Emergent Intelligence in Nervous Systems. We seek to increase the number of women in areas where they are underrepresented and therefore explicitly encourage women to apply. Furthermore, we are committed to increasing the number of individuals with disabilities in its workforce and therefore encourage applications from such qualified individuals.