575 computer-security "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "St" PhD scholarships in United Kingdom
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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic
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across sociology, anthropology, migration studies and science and technology studies (STS). There will also be opportunities to engage with comparative insights from the Netherlands and Sweden. Funding and
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students for The EPSRC Centre for Doctoral Training in Diversity in Data Visualization, starting October 2026; Studentships are available at City St George's and the University of Warwick . Science
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online via the above 'Apply' button. Under Campus, please select *Loughborough* and select Programme *Mechanical and Manufacturing Engineering*. Please quote the advertised reference number ‘SF-ST-2026’ in
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this potential through a national study of HRHIs in England, complemented by two in-depth case studies of St Bartholomew’s Hospital and the Thackray Museum of Medicine . This combination of breadth and depth will
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imputation Research environment: The PhD student will be embedded in the Population Health Research Institute at City St George’s Tooting. They will also be a member of a broader PhD cohort in the ESRC Centre
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? Mechanical seals are critical components in high-pressure storage solutions for hydrogen and carbon capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational
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St George’s, please confirm on the form that your line manager is aware of your application. Selection process Short-listed candidates will be invited for interview. If you have not heard from us
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). Students will be actively encouraged to engage with and learn from the collaborating institutes in the programme, including Oxford, City St George’s, and London School of Hygiene and Tropical Medicine. Each
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pretrained models, where parameter-efficient methods must balance erasure guarantees against model utility Unlearning as an AI safety primitive — exploring how federated unlearning can contribute to removing