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Field
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Methods Monitoring biodiversity at scale is vital for meeting global conservation targets, yet manual field surveys are too expensive and time consuming. Automated systems like camera traps now generate
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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
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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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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
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close to the nest [1 ] but to better understand foraging, we need landscape level detail. The direction of the project can be tailored, but could include developing and applying Bayesian ML approaches
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Global biodiversity targets are repeatedly not met despite increasing conservation efforts. While in some cases this is due to inappropriate interventions or external pressures, in other cases
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem