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Field
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4‑year PhD position in the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and
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twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
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with reducing and oxidising gas-phase species (e.g. laser-based imaging diagnostics, setup of model reactors, modelling of underlying reactions, multi-scale simulation of reactive fluids, computational
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grades in the field of mechanical engineering, material science, physics, computational science or similar, preferably with a specialization in the field of theory and/or simulation Strong understanding
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, enhance or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition-process
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the area of machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work
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process. By integrating simulation-informed priors into the reconstruction algorithm, we aim to significantly improve both accuracy and sensitivity. The position is hosted at the Fritz-Haber-Institut
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degree / double degree programme Yes Description/content The statistical physics of complex systems is a very broad field ranging from the study of quantum phenomena to the conformational behaviour
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Pathogens, a WHO Collaborating Centre, and a member of the Leibniz Research Association. The Computational Infection Biology Department, led by Thomas Otto, is seeking a highly motivated PhD Student (in data
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PhD programme with a focus on data science and research in the structure of matter. The graduate school covers data challenges from application fields such as structural biology, particle physics