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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety testing Presenting
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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning
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operations, such as data storage, budgets, expenses, assets, and ethics approvals. Key Responsibilities: Conduct independent and collaborative research applying AI and machine learning techniques
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and Knowledge: Experience in computational catalysis and surface science. Preferred Qualifications: Knowledge of kinetic modeling and/or machine learning interatomic potentials. Background Investigation
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, machine learning and AI, statistical computing, big data and AI applications and prediction in biology, medicine and infectious diseases. Potential research projects include (but are not limited
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multimodal data, enabling early detection and personalised interventions in clinical neuroscience. The candidate will take the lead on machine learning and computational analyses, primarily supporting our
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frameworks for advanced property prediction and analysis of inorganic disordered materials. Carry out machine-learning based first-principle calculations aimed at advancing the understanding defect-based
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic