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novel models and tools to improve cancer early detection. They will contribute to Cancer Research UK and NIHR funded projects, including studies using large healthcare datasets, such as CPRD, focussed
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understanding of cancer risk and developing novel models and tools to improve cancer early detection. The successful candidate will contribute to Cancer Research UK and NIHR funded projects, including studies
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application of conditional diffusion models, flow matching techniques, or related generative approaches, as well as experience working with probabilistic (Bayesian) methods and statistical modelling. Strong
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well as experience working with probabilistic (Bayesian) methods and statistical modelling. Strong writing and programming skills are essential. A proven record of publishing in high-quality journals and presenting
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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infrastructures. A solid background in beam dynamics in synchrotrons and the corresponding numerical modelling is required. Applicants should have the ability to identify research objectives and subsequently
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(e.g. Python, R) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in
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of translating discovery science into therapeutic applications for cancer patients by enabling effective collaborations between scientists and clinicians to improve cancer detection and treatment. A primary focus
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) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting. Previous experience and