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science, digital modelling, and industrial innovation, this project will put you at the forefront of machining research. Benefits Earn While You Learn: Get a fully funded four-year postgraduate research
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Development of Digital Twin Models for Real-Time Condition Monitoring of Electrical Machines in Electric Vehicle Applications School of Electrical and Electronic Engineering PhD Research Project
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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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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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), 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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oscillations and BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. Ultimately, the software will be
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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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within and related to AI, including deep reinforcement learning, human-in-the-loop machine learning, and multi-agent systems. Dr. Robert Loftin is a Lecturer in Machine Learning at the University
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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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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