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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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partners, (Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD and Alstom); and from EPSRC. The investment, over the first 5 years of operation, was approaching £10M. We are now in our eighth year of
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rational optimisation of plant-mycorrhizal symbioses for more efficient P use in agriculture. The modelling component will involve combining existing models of root system growth, rhizosphere processes and P
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through a competitive process. Studentships will be for four years full-time and will start in autumn 2026. Studentship opportunities are available at Aberystwyth University, Brunel University, Cranfield
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will have the option of indicating this in the equal opportunities form that is part of our online application process. We aim to award a number of studentships to applicants coming from
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in a relevant disciplines in Engineering (for example, Chemical, Material or Process Engineering), Physical Sciences, Economics and Business, or Environmental Science and Sustainability. Prior
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business analysis and process improvement. If you can also add strong stakeholder management skills, then you could be the person we’re looking for. The successful candidate will help Cranfield drive
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on project management. About You You will have a solid background in IT project management and be able to demonstrate competencies in business analysis and process improvement. If you can also add strong
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practical operation a challenge, due to the sensitivity to chemicals used for cleaning, limited tolerance to feedwater composition, and an inability to operate intermittently, with these collective factors
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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