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molecular simulation techniques, specifically density functional theory (DFT) and molecular dynamics (MD) simulations. You are keen to learn new techniques for the development of machine learning interatomic
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closely related discipline. You have a strong background and prior experience in atomistic and molecular simulation techniques, specifically density functional theory (DFT) and molecular dynamics (MD
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dynamics, and density functional theory (DFT) can be combined to accelerate the discovery and design of next-generation organic semiconductor materials with tailored optoelectronic properties. The project
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the
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phonon eigenvalues and transport properties using computational methods (density-functional theory, molecular dynamics, and finite-element simulation). It predicts the intrinsic phononic features
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controlling electrochemical interfaces across scales. Computational PhD position: You will develop an AI-enhanced multiscale modelling framework combining density functional theory, ab initio molecular dynamics
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simulation techniques, including density functional theory (DFT), molecular dynamics, Monte Carlo methods, and free‑energy perturbation calculations. Develop and implement novel computational methodologies and