Research group Vision for Robotics and Autonomous Systems (VRAS) at the Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, is looking for outstanding PhD students. 

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 (https://cyber.felk.cvut.cz/research/groups-teams/vras/) under the supervision of Assoc. Prof. Karel Zimmermann/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 research is co-supervised and sponsored by Valeo AI (https://www.valeo.com/en/valeo-ai/) - one of the leading research organizations that develop state-of-the-art sensors and algorithms for assisted and autonomous driving. Both groups have extensive experience in top-tier research in robotics and computer vision.