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Machine Learning techniques to this data to extract the essential information contained within these trajectories. This will be achieved through the following steps: Develop tools to efficiently generate a
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Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural
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Supervisory Team: Hector Calvo-Pardo; Vahid Yazdanpanah; Tiago Alves (Solar Americas ); Enrico Gerding PhD Supervisor: Hector Calvo-Pardo Project description: Machine learning (ML) holds immense
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developments in machine learning (ML) for phase retrieval. This project is a collaboration with the Ada Lovelace Institute and Diamond Light Source. If you are interested, please contact the supervisor for more
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is expanding its PhD research in the area of medical data analysis. We aim to implement machine learning to analyse atomic force microscopy nanoindentation data towards automated diagnosis of cancer
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Engineering. In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills. This project looks at efficient quantum machine learning
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, more agile solutions. Current machine learning (ML) algorithms identify and predict threats but rely heavily on past datasets, requiring significant updates. Continual learning offers a solution by
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fracture occurrence reported to be 3-fold higher in women than men during military training. This PhD studentship will combine state-of-the-art imaging techniques, mechanical testing and finite element
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challenges faced in the real-world via a Systems Thinking approach. You will learn about the wider challenges of research and innovation within the Defence & Security sector. Entry Requirements This PhD
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learning would have to provide strong justification that they would be able to complete a PhD in this field. Essential selection criteria include: Prior knowledge in computer vision and machine learning