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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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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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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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THE FIELDS OF: ATMOSPHERIC PHYSICS AND CHEMISTRY, ELECTROCHEMISTRY, ELECTROCHEMICAL ENERGY STORAGE (BATTERIES), ELECTRONICS, ELECTRICAL AND MECHANICAL ENGINEERING, HIGH-PERFORMANCE COMPUTING, MACHINE LEARNING
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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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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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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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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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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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