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Field
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This PhD focuses on the development of a novel approach to measuring the traction behaviour of artificial turf, supported by FieldTurf, a global leader in the production of artificial turf for sport
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co-design workshops with men from Black backgrounds and lower income backgrounds, in communities across Yorkshire. You will lead data analysis within the team and help develop targeted recruitment
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, you will explore water-gas shift chemistry to tailor sustainable carbon dioxide and hydrogen concentrations as the first step to produce sustainable fuels. During the project you will design manufacture
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Duration of Award if full time preferred*: 4 years 1st Supervisor: Dr Tao Lyu 2nd Supervisor: Prof Bruce Jefferson This exciting fully funded PhD, with an enhanced stipend of £25,726 per annum
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study: search for the ‘Course Title’ using the programme code: 8020F select ‘PhD Biology (full-time)' as the programme of study You will then need to provide the following information in ‘Further Details
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
on integrated GRMs. The successful candidate will hold a PhD (or be close to obtaining) in Applied Physics, Material Science, Nanotechnology, Chemistry or Electrical Engineering with experience in photonics
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performance, performer training and performing archives, this PhD will examine how engagement with an archive enables performance histories to be taxonomised and reanimated, using Reckless Sleepers’ body
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(or international equivalent) in a relevant subject such as Sport and Exercise Science, Biology, Human Physiology, or Biological Sciences A relevant Master’s degree, for example in Physiology and/or Nutrition Applied
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benefit we can offer is the option for to complete a PhD in the 36-months, if you wish. The opportunity to continue your career at a world-leading institution and be part of our mission to continue science
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either of these species is likely to affect its onward behaviour, and data on these processes will support predictive modelling. The PhD student will be a part of the Surrey/AWE Centre of Excellence in