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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
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flexible to deal with data from different sources. This research and software will support banks and financial institutions with risk management and planning for loss control, and also inform policy-makers
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of a PhD • Expert knowledge of statistical modelling (including both theoretical and computational aspects) and use of statistical software (e.g. R - including programming). • Excellent
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computers”(under the UKRI Guarantee scheme). Topics include: - Quantum many-body dynamics - Quantum algorithms - Quantum-enhanced numerical methods - Quantum machine learning - Tensor Networks - Topological
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of applying geometric approaches and techniques to solve problems. Networking, actively engaging with and valuing other research areas. Published papers/preprints in relevant academic journals. Excellent oral
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Stetkiewicz, Assistant Professor of Sustainable Agricultural Systems, School of Biosciences at the University of Nottingham, and project partners at the British On Farm Innovation Network (BOFIN), using social
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, including conducting surveys and interviews, publishing articles, and supporting scientific networks. Candidates should have a relevant PhD degree (or nearing completion) in development studies, human
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supporting scientific networks. Candidates should have a relevant PhD degree (or nearing completion) in development studies, human geography, rural sociology, anthropology, or political science; excellent oral
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Columbia and Cardiff, we aim to define and understand the functional role of DNA cytosine modification in the development of different pathologies present within the lungs of individuals with Idiopathic
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biodiversity credit methodologies to data on a wide range of species groups. Co-develop, with the project investigators, scenarios for testing the methodologies. Develop and code data simulations, to fit defined