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Field
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of Computational Solid Dynamics Building upon very recent work made by the supervisory team, this FULLY FUNDED 4-year PhD project will investigate challenging aspects such as: (1) exploration of a truly “n” multi
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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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Porous construction materials, such as concrete, brick, or wood, are widely utilised in built environments due to their structural strength, durability, and thermal mass. However, their susceptibility to water ingress can lead to issues such as surface degradation, internal cracking, frost...
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an ideal environment for doing a PhD. Funding Comment This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £20,780 for 2025/26). Additional research expenses
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departments and research centres in the School of Policy and Global Affairs, offering potential for interdisciplinary research in politics, sociology, or criminology. About the programme Upon joining our PhD
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(IoT) device ecosystem. Despite the technology’s potential, however, flexible electronics face numerous technology challenges. This PhD project aims to tackle one of the most critical technology
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This research focuses on a critical challenge: understanding and mitigating hydrogen pre-ignition risks caused by lubricating oils under demanding engine conditions. Hydrogen is a clean, zero-carbon fuel, but its high reactivity makes it vulnerable to pre-ignition. The presence of lubricating...
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We are pleased to announce PhD studentship project in “Advanced Composites Development for Hyper-velocity Impact Protection of Space Satellites Structures”. This is an exciting PhD research
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A 3.5 year PhD Engineering studentship under the supervision of Dr Evgeny Petrov . Blade vibrations in compressor and turbine bladed disks are one of the critical issues in designing gas-turbine
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging