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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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machine‑learning or data‑analytics tools High‑level programming skills (Python, R, Julia) to build, test, and optimize models of geochemical systems Interest in large‑scale computational simulations (e.g
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familiarity with data lifecycle concepts, metadata standards, and data documentation best practices experience in system programming, including scripting and tool development experience in developing machine
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using X-ray and neutron scattering. The main research areas are materials for photovoltaics, proteins in solutions and at the interfaces, complex nano-structured materials and machine learning tools
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and IT develop innovative radiopharmaceuticals and novel tools for functional characterization, improved imaging and personalized treatment of tumors. The Department of Positron Emission Tomography is
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modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as