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strong scientific interests and self-motivation. They will have a degree in physics, mathematics, oceanography, meteorology, or a related science with good computing and numerical skills. Entry
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Butt (j.butt@uea.ac.uk ) for further information. The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships for October 2026 entry. The programme
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in RNA biology, biochemistry, and Oxford Nanopore sequencing. The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships for October 2025 entry
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University) in Engineering or similar relevant science discipline. We also welcome applications from graduates in computational science or mathematics. Note for international and European applicants: details
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, contributing to both science and practice. This is an exceptional opportunity to join the FLOOD-CDT , a world-class doctoral training centre tackling the biggest flood challenges through interdisciplinary
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Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships for October 2026 entry. The programme offers postgraduates the opportunity to undertake a 4-year PhD research project
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altered hydraulic roughness persist — and if so, on what timescales? The student will link fundamental river science with pressing real-world challenges. They will work with a unique decade-long dataset
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to how well they meet the following criteria: A first class (or strong 2:1) degree in any of Engineering, Materials Science, Computer Science, Physics, Biology/Biotechnology, Chemistry/Biochemistry
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each category). Acceptable first degree: Geophysics, Geology, Computer science, Physics, Environmental Science Mode of Study Full-time Start Date 1 October 2026 Funding Information ARIES studentships
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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