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Field
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. The successful candidate for this PhD position will be involved in the experimental and theoretical development of a new technology called Surface Nanoscale Axial Photonics (SNAP), which will enable
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3-year PhD studentship: Scaling-Up Functional 3D Printing of Devices and Structures Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
Overview: Cranfield University invites applications for a fully funded 3-year PhD, supported by the EPSRC DTP and Rolls-Royce. This studentship covers tuition, a tax-free stipend, funding
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to demonstrate a good appreciation of generative AI, especially with respect to its use in video creation. This PhD studentship is fully funded for up to 3.5 years with a tax-free studentship stipend of £20,780
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dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary
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How to apply: uom.link/pgr-apply-2425 This PhD is fully funded; the successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be
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should be made online via the above ‘Apply’ button. Under programme name, select School of Sport Exercise and Health Sciences. Please quote the advertised reference number: SSEHS/SRLJ25 in your application
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Funding Source: EPSRC DTP Sponsor Company: Roxel Ltd Start Date: 1/10/2025 Project Page – Centre Project Overview The aim of this PhD is to investigate RAM formulation chemistry of polyol binders
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.html and here https://www.eng.cam.ac.uk/news/engineering-better-car-experience . Four years of full funding is provided by the UK's Engineering and Physical Sciences Research Council (EPSRC) and JLR
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in