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The University’s Department of Physics invites applications for a PhD studentship funded by the Royal Society and the University of Exeter to commence on 1st October 2025 or as soon as possible
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undertake a mixture of experiment, theory, and numerical simulations in the department of physics at the University of Exeter. The research question is how to effectively shape electromagnetic radiation when
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Location: Students will be based at the CREWW building on Streatham Campus in Exeter. Flooding is the most common natural disaster, impacting billions worldwide. Natural Flood Management (NFM), a nature-based solution opposed to traditional ‘grey’ engineering, offer catchment-level solutions by...
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multiscale metamaterials with sub-200 nm resolution. The research spans fundamental optical physics through to applications, and the student will develop skills in electromagnetic simulation, nanofabrication
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Large Language Models (LLMs) are reshaping how we interact with language technologies, yet many questions remain about what these models actually “know” about language. The University of Exeter is
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to assess the future availability of water resources in sensitive watersheds. The successful candidate is likely to have a background in at least one of the following: physical geography; environmental
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The University of Exeter’s Department of Public Health and Sport Science is inviting applications for a PhD studentship funded by PVolve and University of Exeter to commence on 22nd September 2025
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collected in January 2025 aimed at capturing submesoscale flows and the mixing, stirring and water-mass transformations they generate. This PhD will lead the analysis of physical and biogeochemical data
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of GPUs and/or time in either training or inference procedures, which pose considerable challenges to both academia and industry for widespread access and deployment. In particular, the sampling process of
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The University of Exeter and Oxford Instruments Plasma Technologies are offering a jointly funded PhD position in computational and machine learning modelling of low temperature plasmas. Oxford