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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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Department and aims to develop on-going work on the decision support system. Project aims and objectives This project aims to develop new, scientifically valid applications of skeletal data extracted from
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push the limits of multiphysics CFD for laser manufacturing by developing a next-generation simulation capability for laser drilling (with relevance to additive manufacturing). Your work will capture
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and specialising in flood-risk evaluation, geohazard assessment, and sustainable drainage solutions across the UK, China, and Australia. This research develops a data-intensive, AI-driven framework
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push the limits of multiphysics CFD for laser manufacturing by developing a next-generation simulation capability for laser drilling (with relevance to additive manufacturing). Your work will capture
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into smaller, faster, more energy efficient and cost-effective hardware compared to the current state-of-the-art. The project will align the in-house algorithm-to-hardware development of the Micro-Systems
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-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms, using signal processing/machine learning techniques, to realise all-weather perception in
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent