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– from the modeling of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference
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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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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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learning approaches. A central aspect of this project is the formation of a complex sorption layer—known as the eco-corona—on the nanoparticles and its influence on pollutant sorption. We are seeking to hire
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Nanoscience: Synthesis of Carbon-Nanostructures on Inert Surfaces Biophysics: Learning
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with spectroscopic signatures. Formal requirements include a Master's degree in
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion
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wide range of theoretical perspectives, methodological approaches, and links to educational practice. The interdisciplinary course program focuses on the processes and outcomes of teaching and learning