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Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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machine learning (applied to spatiotemporal data). International and UK applicants are both eligible to apply. Sponsor: This scholarship is funded by the UK Engineering and Physical Sciences Research
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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behaviours? The proposed approach will focus on developing a multi-agent AI framework that integrates traditional penetration testing methodologies with machine learning techniques and advanced generative AI
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established methods of microstructural analysis and mechanical testing with new schemes such as Acoustic Emission for non-destructive assessment of degradation and Machine Learning for development of predictive
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four