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/ year) and cover tuition fees at the Home UKRI rate. Proposed start date: 21/09/2024 to 01/01/2025 Duration of project/funding: 3.5 years We are pleased to present an exciting PhD opportunity in
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(including 38 days off a year) Regular internal seminars, colloquia and PhD/postdoc networks across the department and college, specifically designed to support early career researchers. Personalised career
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administrative activities, with guidance as required, ensuring that project aims and objectives are met. Working closely with the supervisory team, develop and plan research objectives. Present information
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PhD Supervisor: Suan Hui Pu Supervisory Team: Suan Hui Pu Project description: Dehydration in humans can lead to severe health consequences if left untreated. There is an unmet need for an accurate
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industrial settings. From a practical standpoint, new predictive modelling approaches are needed to inform and accelerate industrial process design, as this is an area where much process development occurs
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The conversion of biomass into value-added platform chemicals can greatly alleviate the reliance on fossil feedstock. Biomass alcohol such as benzyl alcohol is one of the important biomass-derived platform compounds and a significant feedstock, which are used to produce a series of value-added...
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. The ideal candidate will have hands-on experience in encoded library design/ screening or other selection techniques. A strong background in molecular biology, protein and peptide chemistry, next-generation
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), accessibility and inclusive design, feminism and intersectionality, trans and gender diversity studies, and misinformation, will be considered. The ideal candidate will have or soon be awarded a PhD from any
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Framework level 12 (PhD) plus track record of emerging independence within a research/professional environment, or alternatively possess professional qualifications and experience equivalent to PhD level plus
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, at the University of Cambridge, UK. The Postdoc will work together with a team of students and research collaborators on the development of learning-based discovery of robot task/environment designs