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hierarchical Bayesian models to cognitive processes Supervisor: Dr Martin Lages Project aims: New variants of hierarchical Bayesian models of cognitive processes can be investigated using simulated, existing and
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immunology, viral ecology, clinical virology, virus epidemiology, mathematical modelling and bioinformatics. PhD: 3-4 years full-time; 5 years part-time; MSc (Research): 1 year full-time; 2 years part-time; MD
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with genetic model organisms, such as yeast, Drosophila or Arabidopsis; or in human biomedicine. Through their research interests in drug development, biotechnology and clinical applications, many of our
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of the project will seek to develop deep learning models (e.g., adapting methods in [6]) based on spatial cellular graphs constructed from these images to predict clinical outcomes. The research will be carried
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University Edinburgh University ST Andrews University UCL Durham Fees and funding Fees and funding Fees 2026/27 UK: £5,238 International & EU: £27,720 Fees are based on the annual fee for full-time study
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Synthetic cell implementation and control using nano-technology Modelling & robustness analysis of large-scale stochastic biomolecular networks Biomolecular network inferring Human control systems More
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backgrounds including: microbiology immunology dental public health epidemiology statistical modeling psychology and behavioural sciences. We engage in knowledge exchange with policymakers, practitioners
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causing mechanisms and explore therapeutic approaches for collagen IV diseases including haemorrhagic stroke. We have brought together a unique cohort of cell lines from patients and animal models with
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compounds in Human Derived Biliary Tract Cancer preclinical models. Supervisors: Chiara Braconi and Sergi Marco Project summary: Biliary Tract Cancers (BTC) are tumours arising from the bile duct within and
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University UCL Durham. Fees and funding Fees and funding Fees 2026/27 UK: £5,238 International & EU: £33,210 Prices are based on the annual fee for full-time study. Fees for part-time study are half the full