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for the ERC Advanced Grant project “Equilibrium Learning, Uncertainty, and Dynamics.” **Positions Available** We invite applications for Doctoral Researchers with a strong background in machine learning and an
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, storage, accessibility/sharing, archiving, publication, and preparing data for machine learning applications. The Research Training Group RTG 3120 offers, subject to the availability of resources, a
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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knowledge of machine learning (e.g., in the areas of object detection and identification, generative AI, etc.) Good written and spoken English skills (min. level B2) Good written and spoken German skills (min
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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
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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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control, and process optimization. You will work on the development of deterministic population balance models, conduct single-crystal and batch crystallization experiments, and apply modern machine