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an increased interest in adapting and developing the latest machine learning methods for the purpose of malware detection, and preliminary results are encouraging. The specific goals of this project include
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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programming models and high-performance computing techniques and machine learning models. Practical experience in the programming of high-performance computing of AI and/or scientific computing applications
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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About the project: From Brittle to Ductile: Machine Learning 3D Fracture Simulations for Extreme Environments Supervisor: Prof, James Kermode, University of Warwick Develop cutting-edge machine
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes