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Description Job Duties: Conduct the computational and theoretical research on magnetic and topological properties in van der Waals materials using Density Functional Theory (DFT) calculations, tight-binding and
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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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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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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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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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, 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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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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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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, 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