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About the project ‘Regulating physician dual practice in Mozambique and South Africa’ is a 3-year research project funded by the UK National Institute of Population Health (NIHR158137 ). The project
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
that not only simulate, but also reason, adapt, and provide explainable insights across safety-critical environments. Key research objectives include: Extract structured engineering knowledge from
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for next-generation gas turbines. These geometries pose manufacturing challenges, particularly regarding heat transfer, microstructure evolution, and defect prevention. Building on recent doctoral research
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Quantum Computing Centre (NQCC), Harwell, Oxfordshire Over the course of the studentship students will be offered a minimum of three months to work at the NQCC to apply their research within the national
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become available, it’s not clear whether these tests are sufficient, and what the most effective method to achieve cervical spine motion control is. The proposed project will simulate various rescue
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PhD title: Mixed Methods Study of Depression Research Clinics as a means of addressing the primary-secondary care gap in NHS service provision. Principal supervisors: Dr Neil Nixon (School
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-scale, high-fidelity CFD simulations, providing detailed insights into transferred arc energy distribution, molten pool behaviour, and their relationship with process parameters. The key research
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Energy Conference, providing exposure to cutting-edge research and industry networking opportunities. 4. Access to High-Fidelity Simulations – The project will use OpenFAST, FAST.Farm, and Digital Twin
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via electron backscatter diffraction (EBSD). We are seeking a motivated researcher with a passion for metallurgy and materials engineering, eager to tackle industrially-relevant problems. Ideally, you
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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early