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balance in the workplace. The Faculty recognises that applicants may seek flexible working patterns which will be considered as part of the recruitment process. please see our working with us website page
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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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methods. It may also include quantitative data analysis. The post-holder will take a role in all stages of this process, from conceptualisation to data collection and analysis, as well as writing up and
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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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. What You’ll Do: innovate: develop scalable RPLD processes to fabricate REDS active mirrors collaborate: work with a dynamic team and engage in interdisciplinary research experiment: gain hands
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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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a new class of active mirror to underpin the next generation of thin-disk lasers. What You’ll Do: innovate: develop scalable RPLD processes to fabricate REDS active mirrors collaborate: work with a
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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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surgery, pathology or any other disciplines related to cancer, but may also be suitable for an individual who does not yet have a national training number and wishes to pursue a career in the cancer field