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Scikit-learn, PyTorch, Openbabel, and RDKit packages. Experience with density functional theory (DFT) calculations. Experience with version control GitHub repositories, and Unix/Linux supercomputing
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a closely related field within the past 3 years and be under 35 years old. A strong background in condensed matter theory is required. The applicant should be proficient in at least one of the
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with density functional theory (DFT) calculations. Experience with version control GitHub repositories, and Unix/Linux supercomputing environments. Good communication skills, including the ability
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, Openbabel, and RDKit packages. 5. Experience with density functional theory (DFT) calculations. 6. Experience with version control GitHub repositories, and Unix/Linux supercomputing environments. 7
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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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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multipurpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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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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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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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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. Experience in asymmetric synthesis and the determination of enantiomeric excess. 2. Experience in using/applying density functional theory (DFT) 3. Evidence of independent funding