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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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represents the first dedicated cancer immunology centre in the UK. The activities in this centre span from pioneering discovery science to applied research and preclinical modelling and, crucially, onto first
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alongside computational biology approaches to map epigenetic changes driven by WNT/GSK3 signalling in ESCs. This collaborative project offers access to world-class expertise, state-of-the-art resources, and
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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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this aim and to collectively, and individually, make a real difference. The role An exciting opportunity for a postdoctoral Research Fellow with a background in mathematical modelling / epidemiology has
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over India with Machine Learning’ (HEPPI-ML), which is funded through the ‘Weather and Climate Science for Service Partnership ’ (WCSSP) programme. It is also linked to the National Institute for Health
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with a strong background in computational modelling or scientific machine learning. You will also have the ability to conduct independent research and a developing track record of publications in
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studies, and computational modelling. They will have the opportunity to visit and work closely with our HFSP partners in Canada (Professor Alastair Simpson) and Denmark (Professor Thomas Kiørboe
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toxicological cross-species metabolomics resources. Drive the in-depth computational characterisation and annotation of metabolomes across diverse model organisms and human cell lines, using multistage
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. psychometric properties; use of R; Growth Curve Analysis; General Linear models). Thanks to your excellent communication and organisation skills, you will liaise with the different teams working on this project