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will be developed. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also
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, you will develop the numerical, geometric and algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically
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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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by: Developing specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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bottlenecks in clinical radiology workflows through observations, structured workflow mapping, and close collaboration with clinical staff. Design, develop, and evaluate AI-based and automated workflow
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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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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum