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Field
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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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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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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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simulation and AI-supported data analysis are central tools. The work is carried out at the Department of Fibre and Polymer Technology and in collaboration with FOI and industrial partners. Qualifications
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focuses on the development of GPU-accelerated, high-fidelity thermal runaway simulation models for lithium-ion battery cells, modules, packs, and complete battery systems. Thermal runaway is a chain
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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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numerical simulations to understand molecular mechanisms of epigenetic regulation by the Polycomb and Trithorax group proteins. Project description Chromosomal rearrangements of the human MLL1 gene
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. Conduct transportation resilience assessment and enhancement studies based on GIS, complex network analysis, and machine learning. Simulate human mobility in response to extreme weather events (e.g
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briefly describe the connection between your past research and the position (max 2 pages), Curriculum vitae (CV) with publication list, Verified copy of doctoral degree certificate or documentation
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) including a complete publication list, Certified copy of doctoral degree certificate or documentation when the doctoral degree is expected to be obtained, Verified copies of other diplomas, list of completed