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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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Multiple self-funded PhD positions are available in Modelling and Simulation (M&S). The project will aim to mature software repositories describing the biomechanics of the human brain. The M&S tools
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This fully-funded PhD research opportunity, supported by EPRSC Doctoral Landscape Awards (DLA) and Cranfield University offers a bursary of £22,000 per annum, covering full tuition fees. This PhD
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of the work such as informing industry standards for aero engine operability. While working on this exciting research project, you will be provided with: A fully funded 4 year full-time PhD - £24,000
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engineering, digital technologies, and systems thinking. The university’s strong reputation for applied research and its focus on technological innovation ensure that this project will be well-supported, with
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This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building on previous research at Cranfield, which has demonstrated the benefits, the project investigates the...
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solutions where improved knowledge of the aero-engine characteristics will be a key consideration. The overall aim of this PhD is to explore novel measurement methods that can improve the assessment of aero
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, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
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of complex, dynamic flows relevant to closely coupled engine aircraft configurations. You’ll join a pioneering multidisciplinary team that values equity, diversity, and inclusion, gaining unique expertise in
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical