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primary supervision of Dr Thomas Ouldridge. The student will develop predictive models of nucleic acid strand displacement rates to allow the rational design of complex networks of ever-increasing
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI
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that predict genetic cancer risk and help explain these risks to people in a clear and safe way. One part of the project builds AI models that can predict the risk of several cancers at once and explain how
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, leading to different models being used. However, in recent years model topologies for automatic speech recognition and many other speech processing tasks have started to converge - driven by research focus
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AI4TECS aims to develop the first AI‑powerAd system that integrates real‑time EC identification using non-target high resolution mass spectrometry data, toxicity prediction, and transformation modelling
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for all. This PhD project aims to develop a robust numerical modelling framework to improve understanding and prediction of heat and fluid flow in deep geothermal reservoirs. Using geological, hydrological
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risk modelling framework that integrates environmental exposures, socioeconomic context, and health data to understand, predict and prevent cardiovascular, neurovascular and other non-communicable
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the consistency of predicted deformations with earthquake focal mechanisms. The first part of the project involves using numerical mechanical models to calculate crustal stresses arising from four main
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image data; Experience in data analysis programs, with special focus on the R programming language; Experience in the development and validation of predictive models applied to the health area
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detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated active learning and create broad, reliable MLFFs. You will