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Center for Scientific Research, France) experiments. DC10 - Objectives: Use DFT, stochastic sampling, and Machine Learning acceleration to model reaction mechanisms and enantioselectivity of chiral
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methods to simulate between functional chemically active surfaces and molecules/liquids. Central methodologies include: static DFT calculations; TBMD and AIMD; classical atomistic and coarse-grained
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techniques, including (TD)DFT and post-DFT analyses, alongside spin dynamics simulations. The primary goal is to investigate how collective excitations and topological effects influence quantum transport and
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at the Humboldt-Universität zu Berlin, where DFT calculations will be performed. running simulations and compare them to experimental results in close cooperation with the experimental group at IKZ. applying
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, or a related field. Have documented experience in some of the following: Computational materials modelling or quantum mechanical simulations (e.g. DFT, MD). Machine learning / deep learning (preferably