639 computer-security "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "Keele University" PhD scholarships in United Kingdom
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digital product passport solutions. Circularise works with firms across sectors, including battery and energy storage, to enable secure and decentralised data exchange. This collaboration provides
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for international students (approx.. 18K /year), please contact Dr. Catarina Rendeiro by email on c.rendeiro@bham.ac.uk, for an informal discussion.
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Warwick: Prof. Zushu Li, Dr Zhiming Yan, Tata Steel UK: Dr Ciaran Martin, Dr Bin Xiao Start date: October 2025 (or earlier) Project Overview: An enthusiastic researcher is invited to join a team to work on
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equivalent) in a relevant science or engineering related discipline. To apply, please contact the main supervisor, Dr Paul Campbell - Paul.Campbell-3@manchester.ac.uk . Please include details of your current
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an interest in sustainability and B2B marketing. How to apply Interested applicants are encouraged to contact Dr. Katy Zafari (K.Zafari@mmu.ac.uk ) for an informal discussion. To apply you will need
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. Enthusiasm for developing creative, qualitative and participatory research skills. Ability to engage confidently with diverse communities. How to apply Interested applicants are encouraged to contact Dr. Kate
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. Applicants should hold a Master’s degree in a relevant area of law and have achieved at least a 2:1 (or equivalent). How to apply Interested applicants are encouraged to contact Dr. Aikaterini Koula (a.koula
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be compared and calibrated. In-silico technique based on Computational Fluid Dynamics (CFD) will also be developed to provide further information necessary for the development of new MRI image scanning
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, Physics, Materials Science , or a related discipline. To apply, please contact the main supervisor, Dr Lea-Langton - amanda.lea-langton@manchester.ac.uk . Please include details of your current level of
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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