381 data-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData"-"BioData" Fellowship positions in United Kingdom
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Field
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interpret data from flood risk models and GIS datasets Disseminate research findings for publication, research seminars etc Contribute to developing and applying flood risk models, and develop techniques
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provide information of what criteria will be assessed at each stage of the recruitment process. Further Information We pride ourselves on being inclusive and welcoming. We embrace diversity and want
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) a research PhD alongside their medical degree or equivalent clinical professional qualification. Application deadline: 30 April 2026 For information on how to apply, please click on the 'Apply' button
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in aeroacoustics, with a strong emphasis on data analysis and a deep understanding of aeroacoustics fundamentals. Demonstrate excellent communication skills, both written and verbal, as evidenced by
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scale. The successful candidate will be responsible for: safely shredding cells using a bespoke instrumented, stabilised shredder; recording data for LCA including mass and energy balances, energy and
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successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information document on our
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provide information of what criteria will be assessed at each stage of the recruitment process. Further Information At King’s, we believe that the diversity of our community and a culture that is welcoming
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, to generate crucial information revealing the cellular roles, functions and underlying mechanisms by which MICL regulates immunity. The focus of this work will be to elucidate the components of MICL involved in
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stakeholder partners, data collection and analysis, and disseminating findings to create impact. This is a key role in a major new programme of social science research, funded by a philanthropic organisation
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, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault diagnosis, and early fault prediction in electric vessels