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A PhD studentship is available to work on Logistics automation. The student associate will work in the Intelligent Logistics Group within the Distributed Information and Automation Laboratory (DIAL
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This PhD project aims to develop and apply innovative photogrammetry-based analytical tools to enhance the spatial and structural characterisation of pharmaceutically relevant materials, with a
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Annual tax-free stipend of £22,780/year for 4 years, full coverage of tuition fees for UK/Home Students, plus training/travel funds Placed On: We invite applications for a fully funded PhD research
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of this contribution, and against the eligibility criteria listed below. As part of this programme we are able to offer several full-fee scholarships to professional learners from across Wales, working with
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How to apply: uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded for home students or EU students with settled status; the successful candidate will receive a tax-free stipend set at
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clinical management. We are seeking a non-clinical PhD candidate with high-level bioinformatics skills to join our internationally renowned, multi-disciplinary team in Newcastle. This position provides
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) change tread pattern (or tread groove depth), ii) aerodynamics by changing the surface texture of sidewall, iii) noise cancelling (vibration), iv) mechanical properties (hardness). Resources: The PhD
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Funding Source: Warwick Industrial Fellowship (WIF) in partnership with Hydrograph Eligibility: UK only Supervisors: Professor Tony McNally, Dr Fengzai Tang Supporting Company: Hydrograph (see Home - HydroGraph ) Start date: 6th October 2025 Project Description – The aim is to develop a...
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Summary The University of Exeter’s Centre for Metamaterials Research and Innovation (CMRI) with UK Government partner Dstl, is inviting applications for a fully funded PhD studentship. The stipend
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust