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efficient and safe trajectory planning in safety-critical scenarios. For this purpose, we focus on modeling and quantifying risks in order to subsequently incorporate them into trajectory planning. The goal
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its maintenance and safety increasingly depend on data. This PhD project will develop new methods that combine remote sensing, physics-based modelling, and Bayesian machine learning to support risk
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interaction-rich scenarios. Ideal applicants will have a strong M.Sc. in machine learning, control, or safety, and hands-on experience with robotics. Apply now: https://lnkd.in/dNjmv835. Deadline: ASAP. We
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interpersonal communication, time-management skills, commitment to laboratory safety & handling laboratory equipment with care, and strong willingness to work in an interdisciplinary environment. We are looking
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, please contact Dr. Robert Georgii (robert.georgii@frm2.tum.de) or Prof. Dr. Skyler Degenkolb (degenkolb@physi.uni-heidelberg.de). The high safety standard of our facility requires the reliability
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failure mechanisms. The performance of the developed methods will be evaluated using real operating data. In addition, it will be investigated how reliability and safety conditions can be taken into account
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on Responsible Data Science. The PhD positions will be at the intersection of Data Science and Social Sciences and will focus on topics such as Explainable & Fair AI, AI Auditing, AI Alignment, and AI Safety in