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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments
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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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simulation environments, numerical methods, or machine learning approaches is an advantage Fluent command of written and spoken English is necessary; German is an advantage but not required High degree
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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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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, 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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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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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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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