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modeling including graphical causal modeling and methodological foundations of the study of technology governance. The research will involve safety cases for AI systems as well as broader aspects of model
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nonlinear aspects. Previous experience in developing advanced constitutive models, particularly for rubber-like materials, is desirable. Informal inquiries may be addressed to Laurence Brassart (email
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/DPhil or be near completion of a PhD/DPhil in a subject relative to Structural Biology, Biochemistry, or Biophysics. You should be driven, have experience in protein production, the analysis
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in the development of numerical models for hypersonic flows and a clear track record in the interpretation of experimental data from ground based hypersonic facilities and their modelling implications
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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
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structural), ECG, and genetics, to model disease trajectories and improve risk prediction in cardiomyopathies. The successful applicant will work closely with the PI to deliver research projects, supervise
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advanced spectroscopic and structural techniques, this postdoctoral project will establish clear correlations and mechanisms linking core properties critical to efficient light-harvesting with basic material
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of extreme events. New modelling capability will be developed to quantify impacts of extreme events on surface melt of ice shelves. These advances will bring a step change over current knowledge of extremes in
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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an industry partnered project for translational drug discovery. The role will involve analysing large scale omics and spatial datasets from both primary patient samples and advanced in vitro model systems