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features enhance binding to the receptors. This information will be carried forward to human tasting panels. You will also investigate how other components of food matrices inhibit binding. You will thus
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Funding information: The studentship is for 3.5 years and provides a tax-free stipend of £20,780 per annum for the duration of the studentship plus university tuition fees. Due to funding restrictions
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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
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nutrition and exercise trials Excellent oral and written communication skills Ability to critically appraise scientific methodology and data Ability to work well individually and as a member of a larger
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the coupled effect on supersonic combustion. This data can then help support future design efforts, which requires validation data where the effects of thermal warpage can be included. To achieve
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. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website . Funding information: The studentship is for 3-years and
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real
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, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data, disambiguate historical agents, and contribute
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to apply For further information please contact: Name: Dr Francesco Fanicchia Email: Francesco.fanicchia@cranfield.ac.uk If you are eligible to apply for this studentship, please complete