640 engineering-computation-"https:" "https:" "https:" "https:" "https:" "U.S" PhD positions in United Kingdom
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may require an ATAS (Academic Technology Approval Scheme ) clearance certificate prior to obtaining their visa and to study on this programme. How To Apply Please read and complete this document as
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up to a £5k/annum research training support grant for the full duration of the 4-year programme. Metal-ligand multiple bonding is a burgeoning area for making chemically novel structural motifs
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This PhD forms part of an ongoing collaboration between IBM and the Department of Engineering at the University of Cambridge, centred on the mathematical and computational modelling of Earth and
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engineering, digital technologies, and systems thinking. The university’s strong reputation for applied research and its focus on technological innovation ensure that this project will be well-supported, with
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are welcome to apply but will be required to cover the difference between Home and International fees. International applicants may require an ATAS (Academic Technology Approval Scheme ) clearance certificate
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(Centre for Doctoral Training in Modelling of Heterogeneous Systems) at the University of Warwick is an innovative, interdisciplinary fully funded PhD programme that brings together science, engineering
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. The successful candidate will work closely with the project sponsors to deliver the outputs and will have access to a bespoke training programme. This project will deliver a model that integrates water quality to
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experimental and computational, to understand and predict microtexture evolution during processing. The balance between experimental and computational research will depend on the profile of the successful
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with a background in chemical engineering, physics, soft matter science, computational chemistry, and/or related disciplines. Early applications are encouraged, as the position may be filled before
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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