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Field
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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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machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for
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networks of these devices we will use digital twins; machine learning models trained to predict physical systems but are differentiable. This project will advance the machine learning methods, particularly
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Recent years have witnessed significant strides made by machine learning-based computer vision, thus enabling machines to interpret and understand visual information. However, most machine learning
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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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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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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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learning called Machine Listening. The PhD project brings together world-leading audio experts to develop state-of-the-art techniques as a widely-applicable embedding of acoustic environments. Based in