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Field
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clinical trials to assess its ability to measure hydration state. This project would use data from WearOptimo’s hydration sensor and develop novel Bayesian methods to model hydration state. How can hydration
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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline
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, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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field. Preference will be given to candidates with: Previous experience in machine learning-related aspects of computational neuroscience, specifically with approximate Bayesian inference, and function
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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spans from advanced theoretical and methodological Statistics (classical and Bayesian) to diverse applications, allowing for comprehensive research approaches. Our members work on Design of Experiments
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more information for a given experimental budget. Efficient active learning depends on the careful co-design of experiments and inference algorithms. You will explore topics such as how to elicit
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary