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of Manchester and the University of Birmingham. The project belongs to the Mathematics for Safe AI funding stream, which aims at assessing how we can leverage mathematics – from scientific world-models
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Stappers who leads the MeerTRAP, TRAPUM and PSS-SKAO teams at the University of Manchester, and will be a member of a team of developers with international industry and academia backgrounds. The team
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deep learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows
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of Manchester and the University of Birmingham. The project belongs to the Mathematics for Safe AI funding stream, which aims at assessing how we can leverage mathematics – from scientific world-models
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm
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input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of
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Research theme: Chemometrics The project is jointly funded by the Community of Analytical Measurement Sciences and the University of Manchester Department of Chemistry. The successful candidate will
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at RAICo One Facility in West Cumbria. The applicant will have a unique opportunity to work in close partnership with robotics, control and AI experts from the University of Manchester, Sellafield Ltd and
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, stress markers, EEG, and ECG — will be collected by VR headsets and IoT devices. ML algorithms will analyse this data to identify trends, project risk factors, and propose tailored treatments. By combining