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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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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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-generation defensive capabilities. The project focuses on moving beyond siloed detection methods to create a unified, multi-modal framework for identifying AI-generated threats. Its core aim is to develop a
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to discover the fundamental mechanisms responsible for damage and deformation. About the host University and Through-life Engineering Services (TES) Centre Cranfield is an exclusively postgraduate university
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platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a
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members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary
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and safety throughout the supply chain. Working within the Magan Centre of Applied Mycology (MCAM) in the Faculty of Engineering and Applied Sciences (FEAS), the student will benefit from access
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We invite applications for a self-funded PhD to explore innovative research in the development of human-centred embodied multi-agent systems that able to compensate and augment human capabilities in
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. This project leverages and advances these trends, targeting the development of multi-functional coatings to enhance EV battery safety. This PhD will design and characterise intumescent, dielectric, lightweight