520 data-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD positions in United Kingdom
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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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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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date 1 October 2026 Additional Funding Information This PhD project is funded for four years by Big C with a contribution of £50,000 supported by the John & Pamela Salter Charitable Trust. Funding
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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
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language requirement (Faculty of Science equivalent: IELTS 6.5 overall and 6.0 in each component). Mode of study Full-time Start date 1 October 2026 Additional Funding Information This four-year studentship is funded by
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information, please contact main supervisor; Prof Michael Greaney (michael.greaney@manchester.ac.uk ).
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with a wealth of social and networking opportunities. How to apply For further information please contact: Name: Prof. David MacManus Email: d.g.macmanus@cranfield.ac.uk Phone: +44 1234 754735 If you are
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, schedule experiments, collect and analyse data, present findings and oversee day-to-day running to project. Adherence to Good clinical and research practice guidelines will be required. The successful
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data