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22.12.2025, Academic staff We are an interdisciplinary team at the Chair of Safety, Performance and Reliability for Learning Systems, and we are looking for exceptional postdoctoral researchers to join our team. The postdoctoral positions will be full-time (100%, TV-L E13). We are committed to...
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adults actively seek, select, and evaluate information to learn about the world. The lab combines behavioral, computational, and cross-cultural approaches to study curiosity, exploration, and reasoning
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We are seeking an outstanding candidate for a Postdoctoral position in the field of robot motion and control algorithms for soft material handling, starting immediately. We are looking for a highly qualified and motivated individual with a strong academic background in robotics and a keen...
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14.12.2022, Academic staff The BMBF-funded position is part of the CoMPS project, which is a multidisciplinary project combining the fields of mathematics, computer science, geophysics, and high
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24.02.2022, Academic staff The Munich Quantum Valley aims at developing a full quantum computing stack, from the application level to the physical quantum hardware. Within this interdisciplinary
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of medicine and computer science at TUM, as well as the Munich Center for Machine Learning (MCML). It is a great place for interdisciplinary research between medicine and data science. We are looking for a Post
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problems or machine learning more broadly. We are looking for candidates with strong mathematical skills and interests. A requirement for the position is a master’s degree in electrical engineering, computer
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remote sensing data using High-Performance Computing at LRZ Publication of the results in scientific journals Assistance in teaching REQUIREMENTS: An above-average degree in mathematics, computer
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related field (e.g. battery technology, automotive engineering, electrical engineering) and have previously completed your master's degree in a technical field, such as mechanical engineering, computer
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding