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2024, Nature) and will integrate modelling methane exchange in upland trees with the LPJ-GUESS dynamic global vegetation model to identify past, current and future changes in this new-found methane sink
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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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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interpretation. This role will involve working closely with civil engineers, computational modellers, Physicists, and geophysicists to translate raw sensor outputs into actionable insights. Role Summary Lead data
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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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and 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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for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and
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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