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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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the Birmingham MC40 cyclotron facility. This experimental campaign and the new data/insights will allow you to validate structural integrity models of ASSs linking meso- and macro-scales. During this PhD project
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Project advert Modern artificial intelligence (AI) increasingly relies on combining multiple sources of information, such as sound, motion, images, and sensor data, to achieve robust and intelligent
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manufacturing sectors, from SMEs to large global manufacturers. For details, visit the MTC website . Entry requirements: A 1st or high 2:1 degree in computer science, manufacturing/industrial engineering, data
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skills, which will be required to lead the development of automated data analysis and modelling. The student will also undertake a research secondment to coral reef monitoring sites at NEOM for three
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. Information session We are inviting you to attend an online information session about LUMS PhD programmes on 13th January 2026, 12:00-13:30 (GMT). Prospective students will obtain information about our PhD
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Are you passionate about artificial intelligence, data analytics, and cutting-edge engineering applications? Join our interdisciplinary research team and contribute to the future of Non-Destructive
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date is October 2026. We recommend that you apply early as the advert may be removed before the deadline. A 3.5-year PhD studentship in advanced NMR and data analysis is available in the University
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past, present and future events to constrain extreme value distributions spanning hundreds to thousands of years. About HetSys: Harnessing Data, Modelling and Simulation for Real‑World Impact HetSys
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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