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successful candidate will conduct Density Functional Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems, particularly under complex chemical environments
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waste-heat recovery systems; Undertake density functional theory calculations and finite element analysis to guide the selection and development of thermoelectric materials and devices, while contributing
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methods such as molecular dynamics (MD) or density functional theory (DFT). • Experience with energy storage devices or materials • Knowledge of battery electrolytes, including water in salt aqueous
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methods such as molecular dynamics (MD) or density functional theory (DFT). • Experience with materials simulation tools or software is preferred. • Knowledge of battery materials, including
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have experience of using molecular dynamics and/or density functional theory methods and a proven ability to structure, manage and work with quantitative data. You will be able to evidence designing and
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of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials. We welcome applicants with a broad range of research interests and experiences who
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of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials. We welcome applicants with a broad range of research interests and experiences who
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] - Dynamical Mean-Field Theory (DMFT) and Density Functional Theory (DFT) - Density Matrix Embedding Theory (DMET) - Density Matrix Renormalization Group (DMRG) - Quantum Monte Carlo (QMC) - Tensor network
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that operate at room temperature. In this project, Density Functional Theory (DFT) calculations will be used to simulate defect structures of h-BN, like substitutional, vacancy and interstitial defects