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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins Classic finite elements approach (FEA) approximate the shape of the model using elements with planar faces, therefore
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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within fusion reactors, especially plasma-facing materials (PFMs) exposed to intense heat fluxes and energetic particles. Understanding and predicting how these materials degrade under such conditions is
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
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Climate change leads to more extreme weather in the UK and triggers public health responses. However, the impacts of extreme weather at the household level has been largely undocumented and may lead
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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conditions. The specific aims are: To optimise large-scale production of a high-value carotenoid compound that is naturally released in nanoparticle form by a marine alga. Develop a mechanistic understanding
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this astonishing picometre fabrication precision. Further aims of the project include: Theoretical modelling of nanoscale effects and processes in SNAP Development of experimental methods of picometre-precise
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to accurately forecast properties for new alloy compositions or processing conditions. Second, capturing and incorporating microstructural features into ML models presents another hurdle. Microstructure plays a
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of waterlogged conditions, peatlands are projected to be particularly impacted by future climate change, through changes in both temperature and precipitation. Bioclimatic envelope models predict significant loss