169 parallel-computing-numerical-methods positions at Technical University of Munich in Germany
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degree in a technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and
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technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and behavior planning
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. The main focus is developing and characterizing metallic high-performance materials for/through additive technologies using experiments and computer-aided methods. Furthermore, the chair is dedicated
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of AI. The ideal candidates will have a background in computer science, statistics, mathematics, or related fields, as well as an interest in social science research methods and theories. The PhD
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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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(ML4Earth). AI methods, 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
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mobility systems through practical and laboratory tests as well as sophisticated simulations. We not only publish research results gained at numerous conferences and in journals, but also make our software
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the courses Advanced Mathematics 1–2 and/or Statistics at the TUM Campus Straubing. Your profile: Above average master’s degree in mathematics or (theoretical) computer science with a focus on discrete
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academic supervision from Prof. Henkel. You will participate in the doctoral program of the TUM School of Management; after about a year, there is the possibility to apply for the School’s Academic Train-ing
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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