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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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motor concepts and provide technical leadership for research initiatives focused on high power density, efficiency, reliability, manufacturability, thermal performance, and reduced reliance on critical
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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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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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simulation techniques, including density functional theory (DFT), molecular dynamics, Monte Carlo methods, and free‑energy perturbation calculations. Develop and implement novel computational methodologies and
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The GCSC encourages you to present and publish your work early on. Assistance is available through workshops and coaching sessions on academic writing and conference presentations. Our e-journal KULT_online
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agreed upon with their supervisors. Full-time / part-time full-time Programme duration 6 semesters Beginning Winter semester Application periods The following is valid for applicants from: all countries