580 professor-computer-science-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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Application deadline: All year round Research theme: Inorganic Chemistry How to apply: uom.link/pgr-apply-2425 Fully funded PhD, home tuition fees and a tax-free stipend starting at UKRI rate of
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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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, chemists and computational experts pioneering research using an advanced, optogenetically controlled human-based nerve-muscle model. Using functional, molecular and metabolomics technologies, integrated with
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extreme threats to national security. AWE has pioneered advancements in areas including physics, engineering, materials science, and high-performance computing. Together we’ve helped shape the UK’s
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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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, chemical, biological or computational science, an interest in theoretical modelling and curiosity about engineering complex biological and biochemical systems. The opportunity to study for a PhD at a world
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First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics, Mathematics, or a related discipline, and demonstrate
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relevant PhD programme via the King’s Apply portal ahead of the following application deadlines: Overseas fee status: 1 June 2026 Home fee status: 25 August 2026
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(with an average programme mark of no less than 65%) or international equivalent . Candidates should have a strong background in marketing, public management, sociology, or public health. Experience in
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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