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this multidisciplinary project, the student will develop expertise in: Direct collaboration with leading medical imaging and materials science partners. Access to Cranfield’s advanced materials characterisation and
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is critical for a wide variety of engineering and environmental applications, remains unexplored. You will join a team of researchers that experimentally quantify the flow-physics of fundamental
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-making process. Research Objectives Model Learning in Dynamic Contexts Investigate the use of reinforcement learning for constructing and updating probabilistic world models (transition and observation
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October 2026 Award Duration 4 years Application Closing Date 16th January 2026 Sponsor EPSRC Supervisors Dr Chris Bruce Eligibility Criteria We are adopting a contextual admissions process. This means we
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concern is the presence of marine organisms that settle and grow on submerged structures – a process known as biofouling. Biofouling will increase drag and mass loading on dSPCs but will vary with many
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between
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straightens the fingers but does not treat the underlying biology; recurrence is common and repeat operations carry escalating risks to hand function. With genetic risk variants now linked to the disorder
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(SMEs) in the health and life sciences (H&LS) sector to embed and accelerate sustainable manufacturing in their businesses. Integrated research in this programme will generate evidence-based guidance
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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profile We welcome applicants with backgrounds in computer science, applied mathematics, or engineering. Essential: strong Python, deep learning experience (PyTorch), and foundations in calculus/linear