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Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace, mechanical
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Trust. The successful candidate will work closely with the PI and a PhD student within a larger cross-disciplinary team to construct a quantitative computational model of carbonate biomineralisation
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of developing a computational model to simulate hydro-sedimentary dynamics and morphology between restored patches to explore conditions favourable for restoration. Validated with data on vegetation cover and
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limits map which describes the performance of a turbine wheel with respect to the microstructure through simulation and empirical testing. The aim of the project is to develop a modelling and simulation
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satellites, with the potential for travel to test instrumentation in ideal locations. Additionally, the simulation work will focus on developing computational models to validate instrumentation and optimising
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
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electroencephalography (EEG), behavioural tasks, and computational modelling, the student will examine how these interventions influence brain dynamics, cognition, and subjective experience.
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Funding: School of Computer Science studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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Computational Fluid Dynamics (CFD) and Conjugate Heat Transfer (CHT) modelling, which captures both the fluid & solid domains, as required to develop this understanding for engine-representative geometries and