76 computer-programmer-"Multiple"-"U"-"Humboldt-Stiftung-Foundation"-"Prof" positions at Technical University of Munich
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technologies to fundamental physics questions. The advertised positions will be part of the project “QS-Gauge: quantum simulation of lattice gauge theories”, funded by the Emmy Noether programme of the DFG
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- PhD student in quantitative verification interested in co-developing Automata Tutor - main developer of Automata Tutor Positions in the Formal Methods for Software Reliability group of TU Munich led by
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academic assignments at the chair What we look for in you Completed master’s degree in computer science, transportation, or related engineering fields Solid background in generative AI, machine learning, and
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writing. Drive the publication of research results in top-tier robotics conferences and journals. Requirements Ph.D. in Robotics, Mechanical Engineering, Electrical Engineering, Computer Engineering, or a
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., for quantum computing, microfluidics, or conventional circuits and systems. Our focus on interdisciplinary partnerships and networks will enable you to meet many interesting people (at places all over the world
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of Orthopaedics and Sports Orthopaedics and the Institute for AI and Informatics in Medicine. We work at the intersection of artificial intelligence, medical imaging, and clinical practice, developing methods
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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for the School’s PhD program. We offer regular public lectures and symposia, weekly discussion groups, and visiting researcher programs. We maintain close collaborative ties to other parts of TUM as
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accessible to users from science and industry Your qualifications: ■ Master’s or equivalent graduate degree in computer science, artificial intelligence, machine learning, mathematics, statistics, data science
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing