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implementation of a coupled Computational Fluid Dynamics (CFD) and fire dynamics model within the OpenFOAM framework. This model will simulate battery thermal runaway and subsequent fire propagation. The
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methods to accelerate and improve the robustness of microwave device modelling and inverse design. Publish research outcomes in top-tier journals and present findings at international conferences. Assist in
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models, focusing on industrial image analysis Develop advanced deep learning methods for power battery inspection models Design and implement novel algorithms for AI-based CT imaging Lead experimentation
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, alignment, evaluation). Design multi-MLLM collaboration methods (knowledge transfer/distillation, federated learning). Build efficient training/benchmark pipelines and report results with clear metrics. Apply
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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, biologists, computer scientists, biostatisticians, students and other research staff. Prepare oral presentations, posters, and written reports to evaluate data, explain analysis methods, and interpret results
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responsible for: Supporting empirical research projects in investments and financial markets, with a strong emphasis on computational and AI-based methods. Designing, implementing, and maintaining research
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Measures using advanced psychometric methods and publication and dissemination; Establishing an independent research programme derived from the current project; Providing support for other collaborative
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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of relevant experience in model development and the design of numerical experiments, development of novel convection schemes (e.g., Develop new functions for both deep and shallow convection schemes