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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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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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advanced characterization methods of inorganic materials and their assemblies, ideally with a focus on battery materials. Demonstrated proficiency in Density Functional Theory (DFT) and/or Molecular Dynamics
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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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, 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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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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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