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prevent failures. This project proposes developing a novel vision-guided robotic machining platform capable of adaptive manufacturing by integrating advanced sensors and AI. The work will involve
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vision systems addressing an urgent industrial challenge with immediate, large-scale impact. The project will take existing knowledge in computer vision and deep learning and apply it directly to a
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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advanced AI/ML methods to neural and behavioural data to uncover the computational foundations of decision-making in humans and animals. Designing novel AI algorithms inspired by brain function, to improve
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record in astrocyte-neuron interaction modelling, published in Frontiers in Computational Neuroscience, PLoS Computational Biology, Neurocomputing, and IEEE Transactions on Neural Systems and
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explore how this programme could also work for children with SEN and complex needs, and engage parents and families more directly. Aim: To explore the impact of a school based PL programme for children with
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opportunities to be active. There are a number of PL programmes to support physical literacy and motor skills in children, however, the next step is to explore how this programme could also work for children with
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through the vessels and how diseases like heart blockages or artery wall damage develop. However, most current computer models used to study blood flow treat arteries as if they are rigid and motionless
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, David S. Friedman, and Megan E. Collins. 2021. “Effect of a Randomized Interventional School-Based Vision Program on Academic Performance of Students in Grades 3 to 7: A Cluster Randomized Clinical Trial
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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision