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). • Eligibility: First degree and Masters in one of engineering and computing fields • Standard departmental requirements: First Class • Experience in physical modelling and machine learning, interest in medical
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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: Applications accepted all year round Details Model predictive control (MPC) has long been identified...
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Development of a Wearable Sensor System and AI-Driven Analysis for Objective Bruxism Assessment School of Clinical Dentistry PhD Research Project Self Funded Dr Thomas Paterson, Dr Ning Ma Application Deadline: Applications accepted all year round Details Project details Problem: Bruxism, the...
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Electroweak measurements with the ATLAS Experiment. School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr K Lohwasser, Dr C Anastopoulos Application Deadline: Applications accepted all year round Details A position is open for an enthusiastic particle physics PhD...
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honours degree (2:1) (or equivalent) in Urban Planning and Design, Computer Science, Geography, GIS, Remote Sensing, Mathematics, Civil Engineering, Architecture, or Environmental Engineering. A master’s
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Early-stage failure prediction in fusion materials using machine learning CDT in Developing National Capabilities for Materials 4.0 PhD Research Project Directly Funded UK Students Prof Christopher Race Application Deadline: Applications accepted all year round Details In fusion reactors,...
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Bio-inspired micro-electromechanical sensors (MEMS) to measure high-order acoustic pressure derivatives for condition assessment of buried water supply pipes School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr Kristian Groom Application Deadline: Applications...
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Machine tool dynamics-based digital twins for real-time monitoring of cutting tool conditions in smart manufacturing
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Multi-Material Laser Powder Bed Fusion for Next-Generation Additive Manufacturing
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. Experience required: 1st or 2:1 degree in Engineering, Physics or other relevant discipline. Experience in computational modelling would be a benefit. Funding Notes This is a self funded project. View