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employment. Starting date: 09.04.2026 Job description:PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 987 - 2026/WP 1 Work location: Hamburg Application deadline
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 2026
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well as machine learning techniques, is central to our mission: Development and application of advanced simulation techniques to explore and identify the fundamental structures and mechanisms occurring in
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-disciplinary research team and gain training in state-of-the-art AI methods such as Bayesian optimization, active learning, and scientific machine learning. The project involves close collaboration with
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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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biomarkers and patient cohorts Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with project partners and regular presentation of results Your profile: M.Sc
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, aligning cutting-edge research with societal relevance and international collaboration. Our work integrates multidisciplinary studies across Earth's system components, leveraging advanced technologies and
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 1 month ago
Job description:Postdoctoral Researcher (f/m/d) in Machine Learning and Surrogate Modeling for Geochemical Systems With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500
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evidence. · Investigating new ways to robustly and reliably evaluate domain-specific IR and RAG approaches to ensure that methods align with user expectations and preferences. · Collaborating with
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date