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This self-funded PhD opportunity sits at the intersection of several research domains: multi-modal positioning, navigation and timing (PNT) systems, AI-enhanced data analytics and signal processing
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This self-funded PhD opportunity focuses on assured multi-domain positioning, navigation, and timing (PNT), integrating data from space-based, terrestrial and platform-based sources of navigation
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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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, fibres and advanced composites for aerospace/automotive applications. In total, he participated in securing multi-million pounds research funding as PI and Co-I from EPSRC, Innovate UK, UK-India Education
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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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. Focusing on adaptive intelligence, which blends human creativity and machine intelligence, the project will develop Multi-Intelligence Agents (MIAs) to facilitate the seamless integration of social factors
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a
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that can be validated with experiments and bottom-up models at multiple scales in order to predict the macroscopic response. Hence, this research will investigate the degradation of metallic materials under
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within the icing group at Cranfield has captured valuable data on droplet splashing, rebound and secondary impingement through experimental research in the vertical icing wind tunnel at Cranfield
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treatment that safeguards microbiological quality and boosts resilience to climate uncertainty. This three-year PhD will deliver the first full-scale evaluation of robotic maintenance for slow sand filters