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vehicles, data centers, etc.). These devices are mostly power electronic interfaced introducing new types of dynamic phenomena and the need for more detailed models, increasing complexity. In addition
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manufacturing (3D printing) techniques. The purpose of the studentship is to develop a next-generation in vitro model of aged human skin to evaluate the cytocompatibility of materials used in maxillofacial
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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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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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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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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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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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