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in the world and will develop skills in machine learning, observational and theoretical astrophysics. For more information on this project please contact s.littlefair@sheffield.ac.uk Information
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Machine Learning Approaches. You will have access to the excellent training opportunities at the University of Sheffield, and will spend time on site at Procter and Gamble. A range of highly desirable
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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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experience of treatment. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies
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, advanced statistical methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and
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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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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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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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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep
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Hangsterfer's Laboratories) EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering for Manufacturing PhD Research Project Directly Funded Students Worldwide Dr Thawhid Khan, Prof