529 data-"https:" "https:" "https:" "https:" "https:" "https:" "KU LEUVEN" PhD positions in United Kingdom
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Science, Physics, or a relevant science or engineering related discipline. To apply, and for more information, please contact the main supervisor; Dr Jack Swallow - jack.swallow@materials.ox.ac.uk . Please
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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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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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supporting the setup, calibration, and running of laboratory experiments, including the preparation and configuration of sensors, data acquisition systems, and associated hardware and software components
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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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, 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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) Payment technologies and the future of money Explainable artificial intelligence (AI) in financial services Integration of FinTech and big data in financial markets and sustainable finance. To discuss the
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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