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Field
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quantum chemistry (QM), machine learning (ML), and high-throughput experimentation (HTE). The objective is to develop a data-driven framework that enhances the efficiency and effectiveness of catalyst
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operating point and environmental turbulent flow conditions. Understanding of all the former conditions is critical to inform industry about the performance of tidal turbines and to develop a machine learning
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oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
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an exciting opportunity to integrate machine learning and statistical methods, focusing on improving the efficiency and scalability of statistical algorithms. The project will develop innovative techniques
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. The proposed new research aims to develop efficient wall models for MHD simulations using advanced deep learning techniques, specifically Physics-Informed Neural Networks (PINNs) and Physics-Informed Neural
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techniques. This research proposes a novel framework that integrates Machine Learning (ML) for structural health monitoring (SHM) and design optimization of CFDST wind turbine towers. The study will focus
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PhD studentship in Machine Learning for Computational Physics and Chemistry, University College London, UK A 3.5-year PhD studentship is available to work under the supervision of Prof Jochen
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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applications such as energy storage, solar, and carbon capture. The project will explore methods beyond traditional density-functional theory (DFT), leveraging cutting-edge techniques such in machine learning
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used in producing fine chemicals, including pharmaceuticals. By leveraging state-of-the-art advances in computer vision and machine learning, we aim to manipulate Particle Size and Shape Distribution