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to improve structural durability, reduce material consumption, and support the UK’s net-zero goals. Funding notes: The position includes a full scholarship for UK-based PhD candidates and a half-scholarship
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research question of this PhD project is: How does extreme weather (e.g. heat waves and storms) affect the indoor environment (e.g. temperature, humidity, air quality), household behaviours and health, and
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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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Project Overview This PhD project is part of an Innovate UK-funded research programme focused on developing a novel ammonia-fueled engine and generator set (genset) demonstrator for harbour and
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, via the admissions portal (accessed via the 'Apply' button above) by midnight (23:59 GMT), 19th August 2025. On the ‘Choosing a programme’ page, please select (Neuroimaging) Research MPhil/PhD (Full
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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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to air pollution in the future, and planning further policy changes. This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series