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working towards a shared goal. You will be responsible for the design and pilot testing of machine learning-based automated ultrasound video analysis models that incorporate temporal reasoning. The research
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responsibility for carrying out research in rough path theory, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure
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,(e.g. bioinformatics, computational genomics) and have Machine learning, and bioinformatic genome analysis experience. computer science or bioinformatics, including bacterial population genomics and/or
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modelling of electrochemical systems, together with experience in machine learning and data-driven modelling. Research Associate: Hold a PhD in Aerospace Engineering, Chemistry, or a related discipline, or
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, Computer Science, Machine Learning, Artificial Intelligence, Engineering, Mathematics, Operations Research, Economics, Finance, or a closely related subject. Preference will be given to candidates with strong
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to have published in leading machine learning conferences or similar venues. One or two PDRAs will be recruited to work within one of, or across, the four research themes: Learning with Structured
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echocardiography dataset called CAIFE consisting of both healthy and abnormal fetal heart scans. You will be responsible for the design and testing of original machine-learning based methods for fetal heart
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the Computing & Engineering Department. The group is very dynamic, ambitious, well networked and delivers state-of-the-art research in a range of machine learning and data science topics, publishing research
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, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational infrastructure for streamed data that enhance contemporary generative and large
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Weather and climate prediction are undergoing a profound transformation. Alongside traditional physics-based forecast systems, machine-learned (ML) weather prediction models, hybrid ML-physics