281 machine-learning PhD positions at Università degli Studi di Napoli "Federico II" in United Kingdom
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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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for Embedded Machine Learning Applications," funded by the NorthEast Launchpad Competition. We are seeking a candidate with a strong background in hardware design and the implementation of machine learning
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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on research in the fields of machine learning, hydrology, and water resources management at various
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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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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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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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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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telemetry equipment. They will be responsible for ensuring high quality twins, and will interact with the project team on the development of effective machine-learning models to be deployed within these twin