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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 20 days ago
functional theory (DFT), including the development and analysis of modern functionals # Solid background in electronic structure theory and quantum chemical methods # Extensive and specialized experience with
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to reduce the amount of required training data while maintaining high predictive accuracy. Methods and Techniques : Density Functional Theory, Machine Learning for atomistic modeling Location : Institut Jean
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. Candidate Profile The ideal candidate should have a background in Solid-State Physics, Polymer Physics, or Physical Chemistry. Experience with density functional theory (DFT) and molecular dynamics software
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Physics, or a related discipline. Experience with recognized computational chemistry software (e.g., Gaussian, Dalton, TurboMole) is highly desirable. Experience in Time-Dependent Density Functional Theory
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mechanisms in porous liquids. Density Functional Theory (DFT) calculations will allow analysis of specific interactions between gas molecules and POSS cages. **Scientific Environment** The PhD will be carried
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computational condensed matter physics. We seek a motivated researcher with expertise in density functional theory (DFT) to study emergent phenomena in quantum materials. Research Areas - Correlated electronic
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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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important - Prior experience with density functional theory or machine learning is desirable - Proficiency in the Python programming language is important, as well as Fortran - Strong written and oral
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic