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Field
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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industrial adoption of high-order CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low
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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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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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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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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