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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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machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and
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on specific use cases, including multi-modality perception systems, to enable testing and validation of robotic manipulation strategies. Implement intelligent algorithms for the robotic execution
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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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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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manipulation strategies. Develop intelligent algorithms for the robotic execution of contact-rich manipulation tasks, enhancing adaptability, precision, and efficiency in a manufacturing context. Collaborate
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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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Predictive and optimization-based control of smart grids: theory and algorithms School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr P Trodden Application Deadline