530 computer-science-quantum-"https:"-"https:"-"https:"-"https:"-"https:"-"Univ" PhD positions in United Kingdom
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of the candidate Essential requirements: A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field Strong coding skills (Python
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clinically meaningful explanations. Finally, the project will develop new evaluation frameworks to assess reasoning quality, robustness, computational efficiency, and real-world clinical usefulness, supporting
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. To apply, you will need to complete the online application form for a full time PhD in Sports & Exercise Science Please complete the Doctoral Project Applicant Form , and include your CV and a covering
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strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
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training in Government and political science research methods as this will be provided on the programme. Research Proposal The project area is broadly defined, leaving scope for the applicant to develop
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PhD Studentships to support doctoral research in the Arthur Scargill Archive at the University of Sheffield Faculty of Arts and Humanities 4 Year PhD Programme Funding Available UK Students View
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and scientific computing. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline
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provide considerable training in chronobiology, signal transduction in plants, microbiology, and data analysis and interpretation. As part of the “MicroClock” programme funded through an ERC Synergy grant
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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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welcome motivated students with a strong background in engineering or science, such as physics, mathematics, computer science, or related disciplines. Skills in mathematics, computational modelling