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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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the underlying molecular structure. Primary responsibilities for this position include the development of computational models to simulate scintillator properties using time-dependent density functional theory and
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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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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
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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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. Experience in asymmetric synthesis and the determination of enantiomeric excess. 2. Experience in using/applying density functional theory (DFT) 3. Evidence of independent funding
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assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics (MD) simulations, enabling the computational investigation
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong