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Field
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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November 2025 or as soon as possible thereafter. This PhD project aims to explore how emerging datasets could provide value to the UK’s insurance industry through a combination of data analytics, modelling
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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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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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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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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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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, 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