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Mobile Edge Computing (MEC) has emerged as a promising computing paradigm to support emerging high-performance applications by deploying resources at the network edge. However, most existing MEC
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a lack of a coherent and mathematically rigorous methodology for how health, environmental and population exposure and vulnerability data can be combined to optimally issue warnings in order to
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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mitigation and adaptation. The CLR focal landscapes are the East Anglian Fens, the Cairngorms, and the Cumbrian Lake District. The Research Assistant/ Associate will work in partnership with colleagues from
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biofeedback in people with CLBP to alter the way their muscles contract and explore the impact this has on pain symptoms and task performance. This project will integrate experimental research with the patient
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their wings for optimal performance. This PhD project will involve the design, fabrication, experimental validation, aeroelasticity modelling, and control of a small-scale wing prototype with a bio-inspired
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. Seminal work by Wong and Gribble (2012) demonstrated that passive proprioceptive training—where the limb is moved by a robot—can induce significant improvements in active motor performance. This implies
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Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge. To address
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intelligent systems aim to optimize power usage without compromising performance, employing strategies like power-aware computing and thermal-aware optimization. These systems are crucial in extending
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of Public Health and Primary Care. The post-holder will contribute to the development and application of a simulation model exploring optimal approaches for iron supplementation in blood donors in England