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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
and 2D memristive materials). As a Postdoctoral Research Associate, you will contribute to research in these areas, bridging state-of-the-art atomistic and mesoscopic simulation methods as indicated
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molecular dynamics simulations across multiple resolutions, most likely from the atomistic to the coarse grained level, using a variety of force fields and computational methods. Run large-scale simulations
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validate the predictions of the ML models by means of atomistic modeling, in particular density functional theory (DFT) calculations, obtaining simulated electronic and emission spectra for the CDs. Finally
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broad experience in the development of electronic structure methods and their application in order to perform atomistic simulations of molecules and materials. These include (but are not restricted
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samples for the study. Further development will be granted by the dialog with advanced atomistic simulations (ab initio and tight-binding) carried out in the laboratory and the lively context offered by
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic
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-scale materials simulations Experience developing and applying machine-learning surrogates for atomistic simulations Excellent verbal and written communication skills Strong collaborative skills and the
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deformation behaviors. This will be supported by numerical simulations developed by the LEM where nanoindentation simulations at the atomic scale will be performed by Molecular Dynamic (MD) as well as finite
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of work already performed by this team. It represents a unique and exciting opportunity to undertake simulations that feed into and from extensive biochemical data in real time. It requires
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at Zürich) and builds on a substantial body of work already performed by this team. It represents a unique and exciting opportunity to undertake simulations that feed into and from extensive biochemical data