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on formal verification and reachability analysis using logical zonotopes, with applications in digital circuit verification and model checking. Candidates should have a strong mathematical background and
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automatic formal verification of cyber-physical systems. The Technical University of Munich is one of the top research universities in Europe fostering a strong entrepreneurial spirit and international
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propose to combine machine learning techniques with formal methods. We will focus on safe reinforcement learning of motion planning problems for autonomous vessels. Motion planning is particularly
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static and dynamic 3D reconstruction, semantic scene understanding, and generative models for photo-realistic image / video synthesis. Overall, the main focus is on high-impact research with the aim
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derivation, analysis, and comparison of numerical methods and simulation approaches for the solution of PDEs Formal proofs, e.g., for convergence, existence, and uniqueness of solutions Fast prototyping of new
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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Euro / year + benefits). 3D Semantic Scene Understanding: The world around us exists spatially in 3D, and it is crucial to understand real-world scenes in 3D to enable virtual or robotic interactions
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Euro / year + benefits). 3D Semantic Scene Understanding: The world around us exists spatially in 3D, and it is crucial to understand real-world scenes in 3D to enable virtual or robotic interactions
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% for PostDocs; 45k – 57k Euro / year + benefits). 3D Semantic Scene Understanding: The world around us exists spatially in 3D, and it is crucial to understand real-world scenes in 3D to enable virtual or robotic
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– 57k Euro / year + benefits). Topics include: Neural Rendering, 3D Reconstruction, SLAM / Pose Tracking, Semantic Scene Understanding, Face/Body Tracking, Non-Linear Optimization, Media Forensics / Fake