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existing data should be prioritised for human review and which potential new data should be prioritised for acquisition. To address this challenge we will advance the state-of-the-art across multiple
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review and which potential new data should be prioritised for acquisition. To address this challenge we will advance the state-of-the-art across multiple component disciplines; in explainable multi-modal
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toward IgG substrates. Computational insights will guide the design of chimeric glycosyltransferases that incorporate Fc-binding domains to achieve proximity-driven enhancement of glycan processing
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process. For further information including key benefits designed to help maintain and support employees' well-being and work-life balance, please see our working with us website pages.
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable
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-ground processes and pollination. The research combines controlled experiments, advanced measurement and multiphysics modelling, and will generate open datasets and workflows to catalyse the emerging field
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may play in stress responses, below-ground processes and pollination. The research combines controlled experiments, advanced measurement and multiphysics modelling, and will generate open datasets and
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, and catalytic processes. The project will use molecular dynamics simulations to understand molecular mechanisms of metal-based antimicrobial systems and biomolecular interactions, supporting the design
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designed to fulfil the requirements for an out-of-programme experience for a specialist trainee in oncology, haematology surgery, pathology or any other disciplines related to cancer, but may also be
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systems, biomolecular interfaces, and catalytic processes. The project will use molecular dynamics simulations to understand molecular mechanisms of metal-based antimicrobial systems and biomolecular