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, cylinders, shells and various prototype two-dimensional and three-dimensional geometries. Such systems have potential applications to sensors, photonics, metamaterials, and displays. Applicants should have
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from motion blur, defocus, and imaging artefacts, which hinder accurate diagnosis. This project aims to restore image clarity by designing intelligent algorithms that recover fine anatomical details
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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photorealistic game worlds. To achieve this goal, we need advances in many areas, from light transport, sampling, geometry and material representations, and computationally efficient algorithms to display
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of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
amounts of maintenance and operational data, from sensor streams to technical logs, yet much of it remains unstructured, fragmented, and underused. Hidden within these records are insights that could help
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face. The project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring
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autonomous by embedding machine learning algorithms to search through different reaction parameters Person Specification Candidates should have been awarded, or expect to achieve, EITHER: A Bachelors degree in
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the