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- Technical University of Munich
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise
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department, Applied Cybersecurity, focuses cutting-edge problems in the foundations and applications of cryptography, including privacy‑enhancing technologies, secure and verifiable computation, and securing
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research program focused on developing next‑generation multimodal imaging systems spanning the mesoscopic to microscopic scale. As part of a major research project and supported by extensive national and
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coordinate national and international in-situ analytical campaigns, performing on-site documentation and analysis in collaboration with partner institutions. • Support students supervision (Bachelor’s
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, quantitative readouts, define performance metrics, and validate results experimentally, helping convert laboratory scale prototypes into a compact, portable system. Your Profile: The successful applicant must
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the possibility of extension, depending on performance and project needs. Qualifications • For Doctoral Candidates: Master’s degree in Computer Science or Mathematics. • For Postdoctoral Researchers: Ph.D. in
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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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for prototyping. Interest and affinity for high-performance computing are necessary for the position. You should have experience with the roofline model and familiarity with a profiler . Experience with GPUs is a
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of the Institute of Automotive Engineering addresses topics related to electric mobility in three teams - from powertrain components such as the high-voltage battery to vehicle design and the integration of vehicles
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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