204 phd-computer-science-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at Technical University of Munich in Germany
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/d) in Energy Informatics, specifically for a DFG project in wind power forecasting using machine learning. You are passionate about applying cutting-edge information technology to solve the energy and
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and an extensive server infrastructure for research Excellent training and career support opportunities (courses, personal coaching, ...) Your qualifications Master’s degree in Computer Science
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observations, focusing on indoor environments. Qualifications: • Applicants for a PhD must hold a Master’s degree in computer science or equivalent • Fluent written and spoken English skills • Proficient C
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and distributed systems. Applicants need to have a strong background and interest in algorithms and/or combinatorics. You ideally should have an MSc degree in Computer science with a focus on algorithms
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networks and their demonstration as proof-of-concept implementation in an experimental 6G testbed. Your qualifications MSc in Computer Science or Electrical Engineering Strong background in networking and
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representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing
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29.05.2025, Wissenschaftliches Personal Two positions for doctoral students in the field of experimental polymer physics are available at the TUM School of Natural Sciences. The Soft Matter Physics
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23.07.2025, Wissenschaftliches Personal The Ecosystem Dynamics Group at the TUM School of Life Sciences, Technical University of Munich studies how ecosystems change in time and space. We quantify
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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06.12.2021, Wissenschaftliches Personal The professorship of Data Science in Earth Observation is seeking six new PhD candidates/PostDocs for its new center for Machine Learning in Earth Observation