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- Machine Learning-Accelerated Quantum Chemical Modelling of Molecular Junctions and Surface Catalytic Reactions PhD Studentship: Taking the forever out of forever chemicals –destruction of per- and poly
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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composites To propagate uncertainty in material behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help
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Understanding plankton biodiversity and ecosystem change by applying machine learning – A CASE studentship Lead Supervisor (DoS): Professor Abigail McQuatters-Gollop Second Supervisor: Dr Clare
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and ML. The University
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in