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addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods. The student will be
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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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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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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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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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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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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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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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infectious disease epidemiology and mathematical modelling in Biology and Medicine. Experience in parameter estimation, knowledge of Bayesian methods and computer programming skills would be an advantage. Good
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods