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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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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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information into versatile benchmarks supporting development of a new generation of assured PNT systems. Positioning, navigation, and timing (PNT) underpin modern transportation, logistics, and critical
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" (Supervisor: Prof Timothy O'Leary) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic
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components, with enhanced functionalities capable of gathering extra optical information. The proposed project is to design flat metamaterial optics [1,2,3] and utilise their increased functionality to develop
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components, with enhanced functionalities capable of gathering extra optical information. The proposed project is to design flat metamaterial optics [1,2,3] and utilise their increased functionality to develop
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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for AI based algorithms. Research experience in these areas will be highly valued. The successful candidate will also contribute to the formulation and submission of research publications, development
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model