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GNSS alone leaves systems vulnerable to interference, spoofing, or outages, particularly in dense urban environments. The development of 6G networks with integrated TN and NTN infrastructures provides
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trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
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very well the behaviour of these cryogenic hydrogen pumps, in order to master their integration into the hydrogen system. The primary objective of this research in collaboration with Airbus is to develop
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the development of specialized hardware architectures capable of efficient, real-time processing. Embedded AI hardware architectures, including neuromorphic processors and low-power AI accelerators
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the operational life of battery-powered devices and reducing the environmental impact of large-scale deployments. Advancements in this area support the development of sustainable technologies across various
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, and energy efficiency. Advanced Manufacturing: Developing and implementing cutting-edge fabrication techniques (e.g., micro-fabrication, polymer, nanoparticle) to realise the designed metamaterial
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validated surface functionalisation methods that significantly improve metascintillator performance, accelerating the development of advanced radiation detectors for ToF-PET and enhancing early cancer
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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vulnerabilities like side-channel attacks and unauthorized access, which can compromise system integrity. Developing robust security measures within AI-enabled electronics is essential for applications in defence
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout