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to implement for the entire network. To address these challenges, the project aims to develop a novel, population-based indirect damage identification system, leveraging data collected on instrumented railway
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maintaining human oversight for quality and accuracy. This real-world case provides a unique opportunity to study how trust in AI systems develops, whether specialists seek to preserve authority in decision
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” because early abnormalities are missed by current diagnostic methods. This project sits at the cutting edge of biomedical engineering, AI, and respiratory medicine, developing a non-invasive, low-cost
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propagation models that incorporate the effects of fire effluents, validated through controlled experimentation. You will develop tomographic inversion methods and anomaly-detection algorithms capable
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prestigious £8M EPSRC Programme Grant on Advanced Integrated Motor Drives (AIMD), a major research initiative launched in 2025. You will work on the development of wide bandgap device-based integrated motor
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-terminal antennas and beamforming operating in FR1 bands and future FR-2, enabling robust terrestrial–satellite integration for safety-critical air mobility services. To develop AI-based algorithms
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of £25,726 plus a research training support grant of £20,000 and 100% fees paid. Overview This PhD project aims to develop a computationally efficient framework for the real-time prediction of river water
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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addresses two intertwined goals: Improving Human Training: Developing adaptive haptic training strategies that help operators refine their skills through real-time skill estimation, multimodal feedback, and
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direction) (White et al., 2021). This likely leads to an underrepresentation of a players training and match exposure. Recently, a sequential movement pattern-mining (SMP) algorithm has been developed