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to improve structural durability, reduce material consumption, and support the UK’s net-zero goals. Funding notes: The position includes a full scholarship for UK-based PhD candidates and a half-scholarship
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-periodic structures, we can precisely control the interaction of radiation with matter, potentially achieving unprecedented timing resolution (sub-70ps) and significantly enhancing signal detection. This PhD
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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Applications are invited for a PhD project within the Faculty of Engineering, in the Centre for Additive Manufacturing research group (CfAM) at the University of Nottingham. The student will work in
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Qualification Type: PhD Location: Nottingham Funding For: UK Students Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780
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to discuss the post informally, please contact Dr Yuanchang Liu () About you The ideal candidate will hold a PhD (or be close to completion) in Mechanical Engineering, Robotics, Marine Engineering, or a
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This fully funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA) and RES Group, offers a bursary of £25,000 per annum, covering full tuition fees. The project focuses
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
of interest, skills and previous experience, motivation for Doctoral Research, future goals, and why you are applying to this project. On the application system select Programme of Study – PhD
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading