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reacting flow conditions. (iii) Identification of the conditions for which hydrodynamic/thermoacoustic instabilities exist. This studentship is part of an EPSRC funded project (EP/Y017951/1 ) and will train
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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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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
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cardiovascular image analysis, but they are limited by their dependence on large, expert-annotated datasets, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where
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will be secondary data analysis, linking reported incidents of violence with weather data to identify correlations between climate variables (e.g., temperature extremes, humidity) and violent crime
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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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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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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where annotations are scarce or unreliable. Recently developed unsupervised learning methods allow