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About the project: Extreme Weather Storylines via Generative AI Supervisor: Dr Tobias Grafke, University of Warwick Generative machine learning techniques, as known for example from large language
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work with large acoustic datasets and
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optimisation tools for aperiodic lattice metamaterials. The research will integrate physics-based modelling with machine learning to enable the efficient exploration of vast design spaces defined by
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between starting your career or researching for a PhD, we’ll back you to study, teach and earn. This is an excellent opportunity for a highly motivated candidate to deliver outstanding research while
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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a mixture of computational, analytical and machine learning approaches to model the heat transfer to fuels and their physical and chemical behaviour, including changes in chemistry and physical
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, experiential learning and opportunities to attend relevant UEA teaching. In addition, you will be a member of the NIHR Academy and able to access the training and resources available to Academy members. You will
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heterogeneity govern transport dynamics and degradation mechanisms during extended operation. A coupled mechanical–transport framework, accelerated through machine-learning surrogate models trained on multiscale