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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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Translational Centre. Plan and publish high quality research, supported in this by the Centre Leads and Deputy leads. Conduct secondary data analyses. Expertise in big data would be valuable as well as advanced
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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