70 assistant-professor-computer-science-and-data-"St"-"St" PhD positions at Technical University of Munich in Germany
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation
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the perspective towards generating data for entrepreneurial seed funding (GO-Bio etc.) for a further development of the technology towards the market. In that sense we also highly encourage candidates that would be
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14.08.2025, Wissenschaftliches Personal PhD (f/m/d) in Polymer Physics/Material Science funded by ERC StG with excellent opportunities for both research and career development. The Technical
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of Engineering and Design. Our teaching and research focus lies on computer-based development of engineering products, particularly on the planning and realization of built facilities using computational modeling
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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on Responsible Data Science. The PhD positions will be at the intersection of Data Science and Social Sciences and will focus on topics such as Explainable & Fair AI, AI Auditing, AI Alignment, and AI Safety in
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) Excellent organizational skills Proficiency in the German language is an advantage but not required We offer A highly interdisciplinary research unit (Heilbronn Data Science Center) including professors
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diagnosis, and knowledge of the operation of helicopter systems. • Confident handling of Python and common data science tools. • Knowledge of high-performance computing and machine learning. • Fluency in
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psychology, anthropology, communication science, bioethics, philosophy, sociology, STS, or a related social science discipline. Applicants should know either qualitative (e.g., focus groups, interviews
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) tailored to the structure and properties of NMR spectral data Supporting the development and application of a self-supervised learning framework for pretraining the foundation model Assisting in the large