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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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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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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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(XAI) to enhance the reliability and applicability of AI algorithms for healthcare applications [2] and/or identifying pitfalls of current AI models using adversarial machine learning. Supervisor Bio Dr
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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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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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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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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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are looking for someone with a strong interest in developing novel, unconventional computing systems to tackle complex machine learning tasks at low energy. You should hold a PhD, or be close to submitting, in
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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