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Field
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atomistic simulations, using both density functional theory and classical molecular dynamics, on ultrathin films of a range of ferroelectric perovskites of technological interest. This position is supported
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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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, Physics, Computational Chemistry, Nanoscience, Chemical Engineering, or a related field. Strong background in modelling (electro)catalytic processes using periodic density functional theory (DFT) is
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, chemistry, computational science, or a related field. Strong expertise in at least two of the following: density functional theory (DFT)/many-body methods, molecular dynamics (MD), machine learning (ML
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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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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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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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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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recent or future doctoral degree recipient interested in conducting research supporting the mission of the U.S. Department of Energy's (DOE), Office of Science, Fusion Energy Sciences research and