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the globe, along with the properties that they carry. However, these currents are profoundly under-sampled by observations and poorly represented in models. One global hotspot of submesoscale ocean
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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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V236E (ε3), which reduce the risk of AD by 2–3 times. This project will use bioinformatics and big data and induced pluripotent stem cell (iPSC) models —cells generated in the lab that can mimic brain
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 12 days ago
for industry placements, site visits, international facility visits and biannual residentials. The Project Are you passionate about sustainable innovation and eager to tackle one of the construction industry’s
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candidates to work with our interdisciplinary and collaborative team on a project on research and development of innovative surface treatments of advanced engineering materials. You will be based in Manchester
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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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, this project aims to develop a novel modelling and analysis approach to address the mathematical and technical challenges of the fluid-structure interaction (FSI) mechanisms globally. The successful PhD
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, into simulation models used for health systems management. The research will provide practical and methodological contributions. The framework will offer healthcare decision-makers better tools for designing
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of the STAMP RSV Program, supported by the Stan Perron Charitable Foundation. The PhD candidate will play an important role in developing models of RSV transmission and vaccination efficacy to inform
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs