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Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks. Interviews are expected to happen in July 2025. Applicants are encouraged to guarantee that referees
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and clinical phenotype information. Using these datasets, you will undertake comprehensive strategies aimed at the characterisation and therapeutic targeting of medulloblastoma biology, focussing
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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discover therapeutic targets relevant to Welsh populations. You’ll also help translate your computational insights into lab-based validation using experimental models, paving the way for new diagnostics and
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magnetic actuations under various magnitudes of field activations. These actuation ranges will be planned targeting potential applications in smart vehicles where following targets will be explored, i
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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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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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device will require an easily operable and user-friendly detection technique to identify targets of interest with minimal training or specialized equipment. The goal is to enable rapid, reliable results