241 web-programmer-developer-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Nottingham
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). This studentship will include a placement at Astra Zeneca, Cambridge and is part of a broader Medical Research Council Programme grant focused to understand mucus regulation in severe asthma. The project will
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to identify areas for research, develop research methods and extend their research portfolio. This work is international in scope and covers a wide range of different substantive issues and contexts. About the
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. About the role You will be responsible for managing the daily activities of a team of recruitment assistants, motivating and developing them to deliver excellent service. You will play a pivotal role in
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Technology, The University of Nottingham. Applicants are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in on-platform manufacturing engineering. The
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Rolls-Royce University Technology Centre (UTC) in manufacturing and On-Wing Technology, The University of Nottingham. Applicants are invited to undertake a three-year PhD programme in partnership
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For You Personal Tutoring Programme. This role is focused on ensuring we deliver the best possible experience for our students. The purpose of the role is to support the Here for You programme, providing a
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
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to carry out the Residential Experience programme as part of the wider ResX team. This is a wide-ranging role and the successful candidate will need to be flexible and confident and able to work as part of a
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deliver initiatives that make our programmes more engaging, inclusive and empowering. A typical day may involve collaborating with student groups, analysing participation insights, meeting with community
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difficult to deploy outside large data centres. This PhD project focuses on developing resource-efficient computer vision methods that maintain strong performance while dramatically reducing computation