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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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) 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
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to identify shared genetic regulation with common, complex diseases to discover new drug targets, indications and disease mechanisms. We have a keen interest in training the next generation of scientists
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use computational approaches to mine natural biodiversity in gene sequences to identify engineering targets to increase lipid content and enhance the water use efficiency. The project will make use
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biotechnological routes that transform waste polymers into target molecules for the nutrition and healthcare industries, collaborating with leading researchers in the UK and Singapore to deliver next-generation
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valuable resource for investigating novel molecular mechanisms underlying gastric cancer pathogenesis, with the goal of identifying new therapeutic targets. You will work closely with academic staff
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to join the Antigen Discovery Group at the University of Dundee. This CRUK-funded project focuses on identifying targetable T cell antigens in early-stage mesothelioma, with the goal of developing a novel
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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