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) in Chemistry, or other relevant scientific discipline (e.g. Physics, Materials Science). Candidates with experience in ab initio electronic structure methods, scientific programming, or scientific
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general, and specifically on systems with characteristic nanoscale features. They use finite element and ab initio software as numerical tools for modelling and analysis. We are seeking highly qualified and
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: Machine Learning Molecular Dynamics. The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first
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-to-olefins, syngas-to-olefins) involve the production of hydrocarbons from renewable raw materials. In addition to periodic density functional theory (DFT), ab initio methods and molecular dynamics (MD
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”. In: J. Chem. Phys. 158.3 (2023). [5] : Kästner, J. “Umbrella sampling”. Comp. Mol. Sc., (2011), 1(6), p. 932-942. Techniques/methods in use: Molecular dynamics, Ab-Initio Molecular Dynamics
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science, or quantum chemistry, with a strong foundation in quantum physics and solid experience in ab initio DFT methods and/or spin dynamics simulations, complemented by strong programming skills. Your
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. Correlated experimental, ab initio and multi-scale techniques are central to our mission: Development and application of advanced simulation techniques to explore and identify the fundamental structures and
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time-scales: ab initio methods for the description of reaction processes, for the determination of electrochemical stabilities and for the optimisation of force fields; molecular dynamics simulations