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comfortable with us from the start. We achieve this, for example, through our onboarding program for new employees as well as by raising awareness of and providing training on diversity and equal opportunity
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plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov solvers
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incoming first-life components. The data you generate will be critical for process planning and manufacturing, and you will work closely with other team members to ensure the efficient upcycling of metal
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Student or Scientific Assistant for Remote Sensing Data Processing and Cloud-based Workflows (f/m/d)
of processing steps and results Your qualifications: Ongoing B.Sc. or M.Sc. studies in geosciences, environmental sciences, geography, agricultural sciences, computer science, or a related field at a German
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: Completed master's degree in mathematics, computer science, physics, geoinformatics, data science, or related fields Ability to work independently, willingness to learn and acquire new skills Interest in
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funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Institute of Organic Chemistry in
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Work group: Institute of Materials Physics Area of research: Scientific / postdoctoral posts Starting date: 09.12.2025 Job description: Scientist (Postdoc) as research software engineer Referenzcode
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as part of the application and selection process, please refer to the privacy policy on our homepage at https://www.senckenberg.de/en/imprint/
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. Information on the DFG Priority Program SPP 2322: https://soilsystems.net/ For cost reasons, application documents or extensive publications can only be returned if an adequately stamped envelope is attached
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Computer Science, Data Science, Physics, Mathematics, Computational Biology, or related fields. Solid experience with Machine Learning / Deep Learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn). Strong