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YBDTP you'll benefit from a regional doctoral training programme that has interdisciplinary collaboration at its core. The aim is to enable you to develop a range of research skills in biological
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Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in Computer Science, Engineering, or Physics, we would like to hear from you
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combining high-fidelity computational modelling with artificial intelligence to overcome key barriers in performance. The investigation will focus on optimising core gas exchange and combustion processes
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Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT website Applications should be submitted by 12th January 2026.
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PhD Studentship: Nanopore Technology for Rapid and Accurate Measurement of Antibiotic Concentrations
for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted via the application page on our website by 12th January 2026.
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for a Programme , create your account and use the link sent by email to start the application process. Please select the PhD in Archaeology. *Important notes* 1) Please quote the reference ‘DRC25-137’ in
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flagship programme of research, Inventing Futures (IFs) . This studentship is for full-time study and covers home-level fees and an annual stipend at the level set by UKRI . International candidates
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@uea.ac.uk The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships for October 2026 entry. The programme offers postgraduates the opportunity
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the UKRI Home Fees for a Masters’ and/or PhD programme, and an annual tax-free stipend (valued at £22,780 in October 2025). As part of your award, you will be able to access additional funding to help cover
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing