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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models
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can successfully predict previously-unknown regulators of seed development through functional genetic analysis of conserved, uncharacterised genes in the model seed plant Arabidopsis thaliana
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Piette and Dr Guy Davies. We particularly welcome applicants with expertise related to atmospheric modelling, eclipse mapping and/or interpreting exoplanet atmosphere observations. The appointment will be
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and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute
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to the discipline Contribute to developing new models, techniques and methods Contribute to enterprise, business development and/or public engagement activities of manifest benefit to the College and the University
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students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental/School research
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the partnership, the Associate will develop the ability to: (i) model and predict fluid flow and system performance for large-scale systems; (ii) assess how different types of fouling may affect the membranes and