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climate will warm and recover in a net-zero future. As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques
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background in machine learning, manufacturing, characterization, and testing of novel high performance multifunctional materials. During the project, you will conduct independent research within the designated
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, materials degradation, or lubrication science. Research that intersects with emerging fields such as digital tribology, machine learning for predictive wear modelling, sustainable materials, or tribological
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the next generation of engineers and scientists. We have a long history of innovation in what we teach our students – and what they teach us. We have a strong interdisciplinary tradition, reflected in
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Learning. For further information about Mathematical Sciences at the University of Southampton please see our webpages: https://www.southampton.ac.uk/research/areas/mathematical-sciences The post is
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statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing to the development of biomarkers and predictive models. A critical part of your