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Field
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Faculty of Science and Engineering’s vibrant doctoral research community, committed to delivering excellent research with real-world impact. This project sits within Manchester Met’s AI, Digital and Cyber
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and emerging applications, such as multi-domain autonomy and aerial mobility. With rising risks to PNT systems from interference, spoofing, and cyber-physical attacks, unified, security-aware integrity
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serious safety risks for older adults, and rapid, reliable detection can significantly reduce long-term injury and improve emergency response. Building on recent advances in vibration-based sensing
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infrastructure. However, the increasing application requirements and rising threats from intentional interferences, spoofing, and cyber-physical attacks expose vulnerabilities in conventional GNSS-centric systems
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machine learning (TML). TML is a cross-disciplinary field that combines machine learning, security/privacy and transparency. As a doctoral researcher your goal is to conduct research in the fast-paced field
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rate). Additional project costs will also be provided. Overview Falls are one of the most serious safety risks for older adults, and rapid, reliable detection can significantly reduce long-term injury
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Warfare, Information and Cyber (EWIC) provides an exceptional environment for this research, offering access to specialised expertise, cutting-edge labs, and a network of defence and security partners
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modelling will be a big advantage, but we seek, above all, a willingness to engage rigorously with challenging concepts. The selected candidate will work closely with the supervisor through informal lectures
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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, well-engineered cyber-range which is available to the project to simulate a wide variety of target systems and set-ups. By combining established cybersecurity practices with adaptive AI-driven techniques