33 multiscale-multi-scale-composite PhD positions at Technical University of Munich in Germany
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12.10.2021, Wissenschaftliches Personal The TUM Professorship for Data Science in Earth Obervation is seeking a full-time PhD candidate on the topic of “Multi-scale Semantic Understanding
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06.06.2025, Wissenschaftliches Personal We are looking for 1 PhD Position in Robotics and AI to work at the Technical University of Munich (Garching Campus) within this multi-disciplinary cohort. We
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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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-22 eV or better, and powerfully test the Standard Model of particle physics. They further constrain CP-violating new physics at scales of 10-100 TeV, far beyond the reach of the LHC. The TUM and the
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Salary Scale (starting at TV-L E13 2/3). Tasks The successful candidate will conduct cutting-edge empirical research in at least one of the following areas natural resources and environment, climate change
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other leading corporates. Transportation data are inherently spatial, temporal, multi-modal, and high-dimensional. Our work addresses the challenges of Perception, Decision, and Explanation (PDE) in
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
, and no knowledge of German is required. This is a full-time PhD position with a competitive salary according to the German TV-L E13 scale (approx. €52,000–€75,000 gross per year, depending on experience
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) at Helmholtz Munich and the Chair of Biological Imaging (CBI) at the Technical University of Munich (TUM) are an integrated, multi-disciplinary research structure and form the cornerstone of a rapidly expanding
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the gastrointestinal system. This large-scale project, with partners at the LSB and the IUF in Düsseldorf (Leibniz Research Institute for Environmental Medicine), combines cellular and molecular biology, sensory science
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enable the model to infer health-related information directly from NMR spectra of human blood. To this end, the model will be pre-trained using self-supervised learning on large-scale, partly synthetic