551 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "St" "St" PhD positions in United Kingdom
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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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for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop
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geophysical and planetary models. IBM faces several such challenges in practice, many of which are currently approached using Foundation-Model (FM) surrogates. However, certain inverse problems arise in data
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and experimental data, the student will develop predictive models of strand displacement reactions in non-ideal settings, in collaboration with the other students within BIOHYBRITE. These areas include
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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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fees. See UKCISA for further information. Starting in October 2026. Later start dates may be possible, please contact Dr Lucia Florescu once the deadline passes. You will need to meet the minimum entry
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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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programme. Nationality restrictions Other eligibility criteria The proposed research project must be within the remit of EPSRC's research themes. Please see EPSRC website for information. Application
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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