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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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: - Apply unsupervised machine learning concepts to the analysis of continuous seismograms recorded in the vicinity of active volcanoes, in order to extract information about the state of the volcano and the
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machine learning-based software applications for materials science Develop code and utilize machine learning to support the automation of characterization and fabrication processes Ensure the integration
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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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computer vision? Would you like to apply AI and machine learning for fundamental research in biology? Then you have a part to play as a PhD candidate. By combining simulations and machine learning, you will
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aspiring data science researcher with an interest in human immunology, causal inference in dynamical systems, and/or computer vision? Would you like to apply AI and machine learning for fundamental research