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and application of methods for simulating magnetism, particularly atomistic and multiscale simulations. The successful candidate will have the opportunity to collaborate with leading experimental and
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learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
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development, assembly, verification, and validation through simulation and experimental testing. The research engineer will also participate in the planning and execution of experiments, analysis of results
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engineering, process engineering or a closely related field, demonstrated through publications. Experience of mass balances, flow simulation or separation and recovery techniques (e.g. distillation, membranes
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of the system is still insufficiently understood. This project investigates the underlying fluid-structure interaction mechanisms and develops advanced numerical methods for high-fidelity simulation
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work
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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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by real-world challenges arising across different application domains. The research focuses on strengthening product/production development processes, and decision-making in complex engineering systems
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towards a sustainable future. Subject description This project focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and