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not ideal. Wearable devices like Fitbit and Apple Watch have become part of daily life, helping people track heart rate, physical activity, and sleep patterns. However, these devices rarely monitor
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algorithms using Monte Carlo simulation and Bayesian inference to distinguish normal tritium losses from suspicious discrepancies during transport, and to develop statistical thresholds that balance detection
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. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms to capture
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require the use of cognitive shortcuts. - To develop and fit computational models (e.g., reinforcement learning, Bayesian models) to participant data, allowing for a precise, quantitative definition of
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that can automatically capture and classify these features, we aim to create rapid, accurate, and objective tools to support clinicians in early diagnosis. Research Plan & Student Role: The project will
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Rail Grinding Performance Mapping using Novel Rail Surface Quality Characterisation (C3.5-AMR-Taylor)
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Overview We have exciting opportunity to join a multi-centre study to track disease progression in motor neurone disease (MND). The study will use high density surface EMG and will focus
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safe to germinate? Objectives: Germination is a high-stakes decision for C. difficile: too early risks destruction in a hostile environment, too late risks missing the chance to colonise a new host. We
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Development and Validation of a Multimodal Wearable Headband for Objective Bruxism Monitoring Using Machine Learning (S3.5-DEN-Boissonade)
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the virtual embryo; and Bayesian Inference to calibrate model parameters and identify developmental control points. You will also gain experience in the simulation-experiment loop, where computational