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Machine tool dynamics-based digital twins for real-time monitoring of cutting tool conditions in smart manufacturing School of Electrical and Electronic Engineering PhD Research Project Self Funded
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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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
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Machine Learning Methods for Enhancing Autonomy of Unmanned Aerial Vehicles in Wildfire Detection and Localisation School of Electrical and Electronic Engineering PhD Research Project Self Funded
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analytics techniques (machine learning) for process control and optimisation. In this project, you will focus on achieving metamaterial behaviour through phase control within the additive manufacturing build
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Improving Deep Reinforcement Learning through Interactive Human Feedback School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Bei Peng, Dr Robert Loftin Application
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Interview Motivated in learning new methodologies and applying new knowledge Essential Interview Knowledge of the approximate Bayesian machine learning (e.g. MCMC) (assessed at: Application form/Interview
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Forecasting the Future of Biodiversity: Cutting-Edge Approaches to Population and Community Dynamics
: How can tools like passive bioacoustics revolutionize wildlife monitoring? We offer cutting-edge training in statistical modelling, machine learning, and ecological forecasting, and our lab works across
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Modelling and characterisation of chip formation and machining induced defects of nano-particle toughened Carbon Fibre Polymer Composites School of Mechanical, Aerospace and Civil Engineering PhD
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), radiological and clinical images. The aim of this project is to investigate the use of artificial intelligence and machine learning in automated detection and segmentation of cancer and its microenvironment