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of acoustic wave propagation in moving fluid and physics-based machine learning (ML) methods. Support experimental design in the laboratory, carry out data processing and to use the experimental results
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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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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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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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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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), 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
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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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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) improve the estimation method using information from the first part of the work and additional constraints, including a Machine Learning approach. (3) inspect how the mismatched expected and measured