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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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to arrange the tuition fees and living expenses. Find out more about fees here . Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study
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and life-cycle assessment will broaden your professional network while a dedicated training budget allows you to attend specialised courses—such as drone photogrammetry or advanced bioinformatics—and to
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materials science and hydrogen technologies. The industrial sponsor, Airbus, is committed to net zero aviation by 2050 and is pioneering LH2 powered aircraft. This partnership provides a unique industrial
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challenge in the UK's Net Zero transition. Current satellite dependent navigation remains vulnerable to interference, jamming and signal degradation, causing serious problems for safe and efficient transport
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researcher with expertise in communication, project management, and leadership. You will build a robust national and international network and acquire advanced knowledge essential for implementing critical
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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are also Disability Confident Level 1 Employers and members of the Business Disability Forum and Stonewall University Champions Programme. Cranfield Doctoral Network Research students at Cranfield