474 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" PhD scholarships in United Kingdom
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reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric
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formed during late-stage deglaciation and subsequent marine transgression. These data will provide critical constraints for palaeoclimatic reconstructions and help quantify the magnitude and style
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, and assess their association with health and production performance. For more information and for an initial discussion please contact Dr Lisa Williams Lisa.Williams2@hartpury.ac.uk The University
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simulation experience; collaboration with leading researchers; and training in advanced characterisation, data analysis, and data interpretation. Number Of Awards 1 Start Date 1st October 2026 Award Duration 4
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to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric archetypes (e.g. curved vessels, bifurcations, side branches
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. Mode of study: Full-time Start date: 1 October 2026 Additional Funding Information This project is fully funded for 3 years. Funding includes tuition fees, an annual tax-free maintenance allowance and a
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through the University’s Apply to Newcastle Portal Once registered select ‘Create Application’ then ‘Postgraduate’ Fill in your personal information and click on ‘Save & Next’ Use ‘Search Course
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studies, the reactivity of MOFs against ammonia and nitrogen oxides will be examined by EPR. This enables information about metal sites, redox transformations, and possible host-guest interactions
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differing aspect of this topic and could be reliant on both the interest of the student and supervisory team, plus what is found through the process of the research. Further information: Applicants should
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap