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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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to leverage innovative machine learning (ML) techniques adept at handling sparse information. The impact of data pre-processing techniques will be assessed with the aim of improving the accuracy of predictions
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: Forecasting Future Environmental Impacts of Photovoltaic (PV) Manufacture Using Machine Learning Techniques This PhD will suit applicants who are keen to make a difference in how we utilise advanced materials
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project is a pioneering initiative that seeks to revolutionize the field of community resiliency and sustainability analysis through a machine learning (ML) and explainable artificial intelligence (XAI
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, Machine Learning, Software Engineering, Chemical Engineering, Civil Engineering, Mechanical Engineering, Robotics, Geotechnology, Operational Research, Computational Physics
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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