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responsibilities Research: Contribute to or lead on the statistical aspects of the development of high quality research bids to evaluate the effectiveness of new health technologies, which is recognised both
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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Directly Funded UK Students Dr Jon Willmott, Dr Matthew Hobbs Application Deadline: 23 June 2025 Details The fusion energy sector must develop methods of remote maintenance where human access is impossible
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manipulation in manufacturing tasks. You will enhance the research, and related activities of the group drawing on your own expertise and that of your colleagues. In addition to strong software development
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of data from in-Situ AM Process Monitoring tools, machine agnostic algorithms will be generated for quality control. Knowledge transfer of the methods developed onto industrial machine platforms will be a
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. Assess the build quality of parts generated through control model algorithms. Validate that methodologies developed are transferrable between different LPBF platforms through evaluation of parts generated
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year round Details Research at Sheffield has been developing models of the railway network from a range of perspectives including smart-grid energy consumption, passenger satisfaction, and integration
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the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required
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, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed
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guaranteed? This project will focus on developing theory and algorithms for MPC applied to the smart grid. The emphasis is on developing implementable (low-complexity) controllers with strong theoretical