64 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Cranfield University
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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failures before they occur, enabling proactive maintenance strategies. Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing
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biodiversity support, cooling, air quality regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue
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current PhD students working in senior roles across government ministries, the armed forces, international organisations, and the defence industry. Student research covers an extraordinary range of topics
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expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here . The Centre has a long
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on multimodal human trust estimation, trust-adaptive decision-making, or cognitive human–machine interfaces that enhance safety and performance in complex environments. This project offers a unique opportunity to
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model development, multi-agent decision-making in autonomous systems, advanced perception, and cognitive human-machine interface that support the human users. Candidates will have the opportunity
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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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regulation and access to nature. By integrating Earth observation, spatial AI, machine learning and socio-environmental datasets, the project will reveal where blue networks perform well across UK towns and
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute