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This PhD project will develop next-generation grid-scale energy storage solutions integrated into HVDC (High Voltage Direct Current) systems at the University of Edinburgh, in partnership with UK
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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full postgraduate research programme Please note that existing postgraduate research students cannot be considered for this funding. Tenable period 3.5 years full-time or 7 years part-time. Continuation
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sustainable aviation fuel (SAF), and importantly, hydrogen. Plastics are comprised of numerous polymers, thus the products of each vary through chemical recycling processes This project seeks to develop an in
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Santos, Dr RG Bryant, Dr Chris Bousfield Application Deadline: 31 October 2025 Details We are seeking a motivated PhD candidate to develop innovative artificial intelligence methods for plant species
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development support. Entry requirements The minimum entry requirement is 2:1 in Biological Sciences, Biomedical Science, Biomedicine or similar. Experience with iPSC culture, microscopy, biophysics research is
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to water, with the Zinc acting as the anode and hydrogen evolution potentially occurring at the steel surface. The successful PhD candidate will work at the forefront of hydrogen materials research, with
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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alternative materials, such as polysaccharide blends and chemically or enzymatically modified starches, to enhance paper strength and contribute to the development of more efficient and sustainable materials in
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to apply early. The second quantum revolution aims to create devices that exhibit a quantum advantage – a step-change in performance gained by leveraging properties of quantum mechanics. The development