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in structure characterization of homogeneous and heterogeneous catalysts using nuclear magnetic resonance (NMR) spectroscopy and density functional theory (DFT) to deduce structure-property
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atomistic simulation methods, such as molecular dynamics, density functional theory, and machine-learning force fields, to elucidate the deformation mechanisms activated by external stimuli. The candidate
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behavior. You will combine density functional theory, thermodynamics, and automated Python-based workflows to generate physically grounded datasets describing oxidation states, defect formation, and surface
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learning theory and benchmark design Experience in robust learning under distribution shift; including domain adaptation, domain generalization, covariate shift adaptation and density ratio estimation