301 computer-programmer-"https:"-"FEMTO-ST" "https:" "https:" "https:" "https:" "Dr" "P" positions at University of Nottingham
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the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed
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one of the world’s leading centres for additive manufacturing research and development, invites applications for a fully funded PhD programme. Metal additive manufacturing is transforming how complex
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about the School is available at: http://www.nottingham.ac.uk/business/ Are you looking for a role that underpins world-leading research and teaching, and gives you the chance to make a positive
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holder will be based in the Plant and Crop Science Division (Sutton Bonington Campus, University of Nottingham, UK) and will work under the supervision of Dr Almudena Ortiz-Urquiza, Dr Sina Fischer, and
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world-class training programme combining research-led innovation with real-world industry application. Students will receive high-level entrepreneurial training provided by Haydn Green Institute, bespoke
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Undergraduate BMBS and BSc programmes. You will deliver teaching and assessment, convene modules, supervise undergraduate projects, contribute to quality assurance and support the student experience. You will
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Graduate Entry and Undergraduate BMBS and BSc programmes. You will deliver teaching and assessment, convene modules, supervise undergraduate projects, contribute to quality assurance and support the student
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, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered). To apply visit: http://www.nottingham.ac.uk/pgstudy/apply/apply-online.aspx For any enquiries about the project please
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Recruitment video at https://mediaspace.nottingham.ac.uk/media/t/1_jdj4s55c To understand our recruitment process (including some handy tips and advice), please follow this link Understanding our application
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through