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AI-electronic systems with applications in aerospace, healthcare, and beyond. As AI systems become increasingly integrated into critical applications, ensuring their security and trustworthiness is
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there is a growing revenue from EPSRC, TSB and EU funded work. At a glance Application deadlineOngoing Award type(s)PhD Duration of award3 years EligibilityEU, Rest of world Reference numberCRAN1141 Entry
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policymakers. Entry requirements Applicants should hold or expect to achieve an equivalent of a first or second-class UK honours degree in materials science, physics, engineering, or a related discipline. The
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at the edge. The project explores advanced topics such as TinyML, neuromorphic design, reconfigurable logic, and autonomous fault recovery, with applications ranging from aerospace, energy, and robotics
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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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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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sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of navigation, AI-enhanced signal and data analysis, and wireless communication systems, with applications in
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compliance and operational integrity. The application of AI in these areas enhances the ability to predict system behaviours, detect anomalies, and streamline certification workflows. AI-driven tools can
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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to identify the material degradation and coatings applications details in extreme environments. A novel techniques/method will be developed with focus on better prediction and more accurate measurement of