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system, including a suitable camera and an embedded system, will enable real-time acquisition of images of the tube carriage, from the side of the carriage, and subsequent image processing algorithms
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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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considered for this project. Application form Closing date 14 March 2022 Decision date May 2022 Additional information ¿Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances