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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous
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, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous experience in any of these areas is useful but not essential. Diving
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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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the student to guide the research, but preliminary ideas include: Exploring whether suggested discharge limits derived from single organism experiments are protective of microbial communities; Using Bayesian
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of machine learning, uncertainty quantification, and Bayesian modelling. They will provide complementary expertise to bridge agentic AI with real-world impact. What We Are Looking from You Background in
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learning, evidence synthesis in public health and statistical genetics and genomics. We are recognised for our strength in Bayesian inference applied to biomedicine and public health. The MRC Biostatistics