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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning
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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 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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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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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position Distributed machine learning takes advantage of communication and
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) to work on the development of an Amazonian Early Warning System (AmEWS) integrating Earth Observation data, process-based ecosystem models, and advanced machine learning approaches. The position is embedded