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Project Title: Intrinsically-aligned machine learning In a truly cross-disciplinary effort, this project, funded by the Leverhulme Trust and in collaboration with the University of Manchester, will leverage
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leading AI researchers, work on cutting-edge projects, and contribute to the university's growth as a hub for AI excellence. The Department of Machine Learning is expanding and we invite candidates at all
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Samuel Kaski’s research group Probabilistic Machine Learning is searching for postdocs to work on AI fundamentals in exciting projects. The work includes collaboration with ELLIS Institute Finland
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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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Digitalising populations of structural systems using machine learning (S3.5-MAC-Dardeno) School of Mechanical, Aerospace and Civil Engineering PhD Research Project Competition Funded Students
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The role will engage in cutting-edge translational research that develops computational models for predicting outcomes in cardiac diseases. This includes a machine learning model to rule out heart
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Robust machine learning using information theoretic approaches for damage detection in complex machines (C3.5-ELE-Esnaola) School of Electrical and Electronic Engineering PhD Research Project
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Topologically constrained physics-informed machine learning for modelling complex spin textures (S3.5-COM-Ellis) School of Computer Science PhD Research Project Competition Funded Students Worldwide
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Adversarial machine learning - Identification and prevention of cyber-physical attacks on infrastructure (S3.5-MAC-Champneys) School of Mechanical, Aerospace and Civil Engineering PhD Research
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Oxford Population Health (the Nuffield Department of Population Health) provides an excellent environment for multi-disciplinary research and teaching and for professional and support staff. We work together to answer some of the most important questions about the causes, prevention and...