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to predictably control and exploit the drop for useful tasks. Aims: 1. Develop computational models to quantitatively predict the response of chemically active drops to the various physico-chemical stimuli
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Some of the biggest open questions in modern physics concern the limits of the Standard Model and General Relativity. The nature of dark matter and dark energy remains unknown, and many theories
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dependencies in cell and primary patient models. Jointly supervised by Dr. Tianyi Zhang and Professors Guy Pratt and Sarah Dimeloe who will provide supervision in genome biology, immunology and immunotherapy and
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the shear-thinning and thixotropic behaviour of mucus. This work will incorporate mucus rheology, microfabrication and testing the resulting device on suitable benchtop intestinal models. You will gain
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for candidates with a background in or demonstrated ability to learn about: Bayesian methods, probabilistic machine learning or inverse modelling. Prospective applicants are encouraged to direct informal inquiries
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not necessary as training will be provided. Experience of computer modelling is desirable but not essential as full training will be provided in an active and well-resourced research group based in
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, sensor design and calibration, finite element modelling, polymer processing, embedded electronics, and ex vivo tissue methods. The University is uniquely positioned to benefit any applicant interested in a
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. Mathematics is the key to unlocking innovation and addressing these complexities. They all require new, predictive models that can link the processing and microstructure of formulated products to their final
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This PhD project will develop mathematical models to investigate population dynamics in biological systems. Combining dynamical systems theory, mathematical modelling, and data-driven approaches
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
, programming, or computational modelling is desirable. Prospective applicants are not expected to be expert in all these areas as training and support will be given where appropriate, but they must demonstrate a