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technologies and develop algorithms and software tools dedicated to accelerating research on multiple levels. We are working at the intersection of computer science, physics, and material science to push the
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lights and virtual traffic signs, and enables sovereign intervention by public authorities. To perform these tasks, it accesses a digital city twin, registers the position of all connected vehicles, and
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of the doctoral project is positively evaluated after the first two years. CMS’s inter-disciplinary team is performing research in the broad field of computational methods for the built environment. Particular
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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
• robotics and/or mechatronics • computer languages C, C++ and Python and interest to work in an interdisciplinary environment are desired. German language skills are necessary for this position. Our offer: We
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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, and especially machine learning (ML) with deep neural networks have replaced traditional data analysis methods in recent years. The Technical University of Munich (TUM), together with the German
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MONAI framework. Working in a Linux environment, with experience of shell scripting, cluster, or cloud computing. Fluency in spoken and written German. We are looking forward to receiving your
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domain shift, e.g. multi-modal data acquired by different scanners and imaging protocols. Publish and present scientific results at international conferences and high-impact journals. Close collaboration
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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