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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 behaviour. The group counts five senior researchers (postdocs and senior lecturers) and two PhD students. On the availability of the group, there is a traffic simulation lab with an advanced driving
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senior lecturers) and two PhD students. On the availability of the group, there is a traffic simulation lab with an advanced driving simulator . The group has several active projects in the field
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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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the group’s existing collaborations. Contributing to the development and maintenance of relevant research infrastructure, including simulation, software, experimental, or laboratory resources
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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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installed at GANIL. You will contribute to the simulation, preparation, optimisation, and further development of the experimental setup and its associated systems. You will also take an active role in
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the project, you will simulate and fabricate your devices and use these for your experiments. You will receive training in all required techniques to ensure a smooth start to your project in our collaborative
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dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex
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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