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infrastructure, we support high-level international research collaborations and diverse teaching opportunities. We also have research groups that develop and apply density functional theory and other theoretical
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thin films possessing the desired magnitude and direction of the polarization. The successful candidate will perform atomistic simulations, using both density functional theory and classical molecular
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functional theory) and high-performance computing. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to
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Contribute to the preparation of scientific and technical reports. Develop and apply methodologies based on Density Functional Theory (DFT) to complex systems. Support simulation tasks and results analysis
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular
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electronic structure theory (e.g., density functional theory), and machine learning based computational studies of molecular and periodic systems. The postdoc will also work within a multidisciplinary multi
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networks and transformers. Practical experience with density functional theory (setups, convergence, interpreting outputs). Strong Python and deep-learning stack (preferably PyTorch); good software practices
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responsibilities for this position include the development of computational models to simulate scintillator properties using time-dependent density functional theory and Monte Carlo methods, analysis and
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lasers is essential. It will be advantageous with experience on pump-probe spectroscopy and/or supercontinuum generation. Experience with density-functional theory is also of relevance. The applicant must
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on plausible solid-electrolyte interphase structures on the extreme surface. The applied computational methods will be periodic semi-empirical methods and density functional theory. The person recruited would