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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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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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related to staff position within a Research Infrastructure? No Offer Description Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing
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Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing application date: 10 June 2024 Start date: July 2024 or as soon as
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usefulness of the forecast, and perception of forecast performance by the public. Statistical post-processing techniques can help to reduce forecast errors by training machine learning models on data sets
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sustainability analysis through a machine learning (ML) and explainable artificial intelligence (XAI) outlook. The project marks a significant advancement in improving public safety against both low-probability
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Project title: Using machine learning to evaluate atomic force microscopy nanoindentation data Supervisory Team: Dr Martin Stolz, Dr Sasan Mahmoodi Project description: The University of Southampton
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Functional lung image synthesis using machine/deep learning: development, validation and application
Functional lung image synthesis using machine/deep learning: development, validation and application School of Medicine and Population Health PhD Research Project Competition Funded Students
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vision, machine learning, deep learning, AI Funding Notes The Faculty of Health is offering a number of scholarships for the academic year 2024/5 for eligible students. This project is being considered
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recognition from smart sensor data and brain-computer interfacing. Supervisor Bio Dr Matthew Ellis is a Lecturer in Machine Learning within the Department of Computer Science. With a background in theoretical