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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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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 University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs), uncertainty quantification, and atomistic simulations within the FNR
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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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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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A Human-Factors Investigation of Automation, Decision-Support and Machine Learning in Clinical Decision-Making Tasks. This PhD project is based within the Human Factors Research Group in the Faculty
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell