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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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challenges in the area of hazard assessment and impact forecasting. The aim of the project is to develop methodologies for forecasting future energy use for various assets and weather scenarios from short term
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cannot capture the dynamic chemical changes in the gut. There is therefore a critical unmet need for a real-time, chronic gut monitoring tool to unravel neuroendocrine gut–brain pathways and guide therapy
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Intermittent streams (c. 50% of world rivers) and headwaters (c. 90% of rivers) are key riverine systems for hydrology, ecology and water quality, which are poorly monitored and lack data (Dugdale
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1. Project overview Electrochemical sensing plays a central role in next-generation health technologies, enabling real-time monitoring of physiological environments with high sensitivity and low
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insole architectures to redistribute plantar pressure and improve comfort. To support continuous monitoring, sensing elements will be incorporated within the insole structure to capture changes that may
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gaps, and to apply and develop different approaches to recording, monitoring, and conserving tracks. This work will form the evidence base for: [1] considering the merits and practicalities for fossil
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personalised, ethnically-stratified risk scores. This is a highly interdisciplinary project at the intersection of machine learning, health equity, and precision medicine. The successful candidate will join a
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; Nguyen et al., 2023). By integrating large scale, multi-modal data and leveraging self-supervised and transfer learning, these models demonstrate satisfactory spatial-temporal simulation and predictions
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to identify priorities for flank monitoring and for the development of site-specific volcanic-tsunami hazard management plans, and to recognise onshore and offshore knowledge gaps. Working with hazard