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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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, 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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power systems in the CoSES lab at the Technical University of Munich. Previous Work https://mediatum.ub.tum.de/doc/1731060/g5zgxaj96lcyhh8gh6le1xbuu.Wetzlinger-2023-TAC.pdf https://mediatum.ub.tum.de/doc
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-MS. (previous works e.g.: https://www.nature.com/articles/s41563-019-0555-5). Ideally, the applicant takes over the co-supervision of selected PhD candidates. We are looking for an individual with a
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networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning for inverse
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Branch Office DRC Building D1, 1102A 19 Dongfang Donglu, Chaoyang District 100600 Beijing, VR China Tel.: +86 010/6590 6656 Fax.: +86 (10)/6590-6393 E-Mail.: [email protected] WWW: http
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learning, safety-critical control, probabilistic modeling, verification, or optimization; Excellent communication skills in English and the ability to work both independently and collaboratively in
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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
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qualifications. Full-time employment is envisaged; models that enable continued employment at your home institution on a small scale are possible. The minimum duration of stay in Munich is 18 months, the maximum
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to the research and development of dexterous end effectors for a new 6G-based teleoperated surgical robotics system. This position stems out of the large scale 6G-Life project (https://6g-life.de/) and will give