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reactions, catalyst surfaces and interfaces, reaction mechanisms, activity and selectivity develop reproducible atomistic simulation and high-throughput workflows using Python, ASE and relevant DFT software
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MLIPs and DFT workflows (e.g., VASP, atomate2). Experience running and scaling simulations on HPC. Broad knowledge of solid-state materials science. Ability to work independently within a large multi
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functional theory (DFT) calculations of NMR parameters, powder X-ray diffraction, and other complementary analytical techniques. The postdoctoral researcher will be responsible for planning and conducting
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simulations. Develop and optimize automated workflows for data generation and active learning, including molecular dynamics (MD) sampling, configuration selection, labeling with reference methods, and model
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considered an advantage: Density Functional Theory (DFT), Molecular dynamics simulations, Machine learning methods for materials modelling, Computational materials science, Organic or hybrid materials
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Responsibilities Theory Quantum transport modeling using NEGF; first-principles materials and interface calculations using DFT (VASP, Quantum ESPRESSO, or equivalent). Atomistic spin dynamics
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; previous experience as local contact at synchrotron XAS beamlines; knowledge of advance characterization techniques like XANES simulation and ab initio calculations (e.g. DFT). Good time management skills
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field. Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results. Hands-on experience
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semiconductors, including conjugated polymers. • Perform molecular dynamics simulations to investigate structural organization and molecular connectivity. • Carry out electronic-structure calculations
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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators