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prepare, review and refine theories as appropriate. About You You will have or be close to the completion of a PhD/DPhil/DClin or other professional doctorate degree in a relevant subject, (e.g
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, develop risk models, and help generate new hypotheses to inform future therapeutic strategies. The role offers a unique opportunity to bridge data-driven insight with translational cardiovascular research
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, and market and protocol design. The postholder should hold a relevant PhD/DPhil or be near completion in one of the following: Economics, Finance, Operations Research, Statistics, Econometrics
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discipline (eg Statistics, Machine Learning, Biostatistics, AI, Engineering) with experience of developing and applying new methods. You will be able to develop research projects, with publications in peer
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an initially solid-like state firsts yields and starts to flow, and in particular on the statistical physics of how initially sparse plastic events in an otherwise elastic background then spatio
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, imaging and timing applications. In this project, we will: Develop Bayesian deep learning methods for event-based data, including single-photon detections and neuromorphic camera data. Investigate Bayesian
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Assistant/Associate will be to lead preclinical research into new treatment strategies for Malignant Rhabdoid Tumours (MRT), a rare and aggressive childhood cancer. You will help develop and characterise
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with experimental collaboration to uncover complex biological mechanisms. Our interdisciplinary work draws on statistical physics, applied mathematics, and close ties with experimental labs. Current
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University of Cambridge, Department of Pure Mathematics and Mathematical Statistics Position ID: CambUK -RESEARCHASSOCIATE [#26302] Position Title: Position Type: Postdoctoral Position Location
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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics