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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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dynamically combine trusted and opportunistic signals. This project aims to develop versatile benchmarks for assured multi-domain PNT systems with advanced integrity frameworks, enabling rigorous evaluation and
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research. A novel DRL-based wind turbine control system that dynamically adjusts parameters for improved efficiency and reduced mechanical stress. A Hybrid AI-Predictive Maintenance model that integrates
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. Although there is a clear synergy between fatigue damage and corrosion, most fatigue prognosis models do not explicitly consider the role of the environment, which is usually reduced to obscured fitting
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advanced technology and business needs, creating smart monitoring systems, predictive maintenance solutions, and digital twins that solve pressing challenges across healthcare, energy, aviation, and
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increasing relevance today due to its ability to bridge the physical, digital, and social domains. CPSS are integral to the modern world, shaping the development of intelligent systems that respond dynamically
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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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integrating advanced vision and language transformers with an Explainable AI (XAI) layer, the project aims to create a robust system for accurate threat identification, providing actionable intelligence
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increasingly important. The aim of the project is to explore the collaborative dynamics of agents within eCPS, with a specific focus on aligning their behaviours towards achieving sustainability goals. Cranfield
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This project is sponsored by EPSRC, Cranfield University and a consortium led by ETN global. ESPRC will provide stipend (£22,000 per annum) and cover UK fees. The consortium will provide supervision