43 computer-algorithm-"UNIS" Postdoctoral positions at Technical University of Munich
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15.06.2020, Wissenschaftliches Personal The 3D Understanding Group at the Technical University of Munich is looking for highly motivated PhD students and PostDocs at the intersection of computer
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
control of such systems, taking particularly into account model uncertainties as well as limitations pertaining to acquisition of data, communication, and computation. We apply our methods mainly to human
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. Requirements: Completed university degree in computer science or applied mathematics, remote sensing, geophysics, physics, or related areas Expertise in computer vision and/or machine learning (deep learning
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for livestock systems in East Africa, and in the subtropics in Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We
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, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since
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the subtropics in Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We conduct experiments in the field
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and satellite-based 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
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engineering, computer science or electrical engineering, with good grades. Experience in scientific work, project proposal writing and team leadership are part of your repertoire. In addition, you are
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computer science. It offers automatic grading and feedback of various types of exercises. The tool has been used at dozens of universities around the world (including 5 times at TUM) and graded almost a million
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communication system 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