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on conferences and in publications Requirements Master’s degree in physics or chemistry, computer science or equivalent Interest in Physics and Machine Learning Good written and spoken English Ability to work both
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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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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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. Qualifications: • Completed academic university degree (Master level) in mathematics, computer sciences, physics or a related discipline • Knowledge of programming, machine learning methods, mechanistic modelling
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performance. They apply and develop a broad range of interdisciplinary technologies ranging from genetics and genomics to structural biochemistry, advanced imaging and computational and mathematical modelling
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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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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Germany Immunohistochemistry and/or RNA in-situ hybridization and light microscopy High degree of motivation, willingness to learn and team spirit Strong sense of responsibility, organisational skills, high
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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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American Studies, North American Studies, Food Chemistry, Chemistry, Computer Science, Physics