47 modelling-complexity-geocomputation Fellowship positions at University of Birmingham
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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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to data collection Operate within area of specialism Analyse and interpret research findings and results Contribute to generating funding Ability to communicate complex information clearly Main Duties
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Ability to communicate complex information clearly Fluency in relevant models, techniques or methods and ability to contribute to developing new ones Ability to assess resource requirements and use
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successfully in a biochemical laboratory High level analytical capability Ability to communicate complex information clearly Fluency in relevant models, techniques or methods and ability to contribute
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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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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