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Field
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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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laboratory-scale turbines. Combined with modern measurement methods, these facilities offer ideal conditions for addressing real-world wind energy challenges under controlled laboratory settings. The position
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This project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI
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atmosphere. Modelling evidence so far suggests that SAI may avert AMOC weakening if properly implemented, but if applied too late, cooling impacts from AMOC collapse or temporary weakening and from SAI
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. The position will involve development of codes/models simulating the nucleation and propagation of stress corrosion cracking in samples under low cycle fatigue conditions, as well as models for linking
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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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contributes to the improvement of climate prediction models. The Atmospheric Chemistry and Atmospheric Microphysics departments are looking for a committed doctoral student to carry out this project. You can
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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