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processing and storage, and supporting compliance with stringent certification requirements. Cranfield University offers a distinctive research environment renowned for its world-class programmes, cutting-edge
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, highlighting its originality and significance. Outlines the methodology you propose to use to carry out your research, including the data sources and source material you plan to use. Suggests what you hope
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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relevance. A digital twin framework for safe, simulation-based validation before deployment in operational wind farms. Develop explainable AI (XAI) frameworks and human-computer interfaces that enable wind
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University offers a distinctive research environment renowned for its world-class programmes, cutting-edge facilities, and strong industry partnerships, attracting top-tier students and experts globally. As an
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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defence and security studies, and is lauded for the rigour, originality, international perspective and policy relevance of its doctoral programme. The Centre has delivered short training and long degree
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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 sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of...
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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing and emerging applications, such as multi-domain autonomy and aerial mobility. With rising risks to...
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This self-funded PhD opportunity focuses on assured multi-domain positioning, navigation, and timing (PNT), integrating data from space-based, terrestrial and platform-based sources of navigation information into versatile benchmarks supporting development of a new generation of assured PNT...