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signs of cardiovascular changes, adaptively model physiological patterns, and identify predictive biomarkers of maternal health. You will develop and apply cutting-edge techniques in: Signal processing
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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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in a discipline related to “Greenhouse gas emissions modelling; Reduced-complexity modelling of the land surface carbon cycle; Climate modelling; Climate mitigation modelling”; demonstrate expertise in
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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: 30 August 2025 Apply now As part of the EMBRACER project external link , you will use advanced models integrating our climate system to human behaviour to study potential scenarios of what could happen
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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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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding