Postdoctoral research position in self-supervised learning in machine perception

Czech Technical University in Prague (CTU) now offers a new fellowship program, the CTU Global Postdoc Fellowship. This new and attractive two-year fellowship-program offers excellent researchers who have recently completed their PhD the chance to continue their research career at CTU. Fellows receive a two year fellowship and become members of a team led by a mentor. The fellowship aims at external international scientists who are currently conducting research abroad. Applicants must have completed their PhD within the last seven years. The fellowship aims at authors (co-authors) of three or more publications in a journal with IF. 

While collecting vast amounts of data from various onboard sensors on contemporary vehicles is easy, providing manual annotations for supervised learning is extremely challenging and consequently expensive. It is especially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme and datasets are large. We leverage the strong prior knowledge about various semantic classes that appear in traffic scenarios in order to construct novel self-supervised losses that allow learning from these unlabeled data. These losses typically encode first-principle models (learning the 3D flow and terrain models from sequential data or learning the 3D shapes of objects) or behavioral patterns induced by traffic rules (learning to predict future motion).  

Group and supervision: Research will be conducted within the Vision and  Robotics Group under the supervision of Prof. Tomas Svoboda. The group has extensive experience with real robotics, such as successful participation in the DARPA SubT challenge (https://robotics.fel.cvut.cz/cras/darpa-subt/) and several state-of-the-art robotics platforms and sensors (https://robotics.fel.cvut.cz/cras/robots/). The Department has access to a high-performance computational cluster dedicated to artificial intelligence research and developments using traditional multi-CPU systems, but also GPUs.