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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 2 days ago
# Completed university studies (PhD) in the field of Physic, Chemistry or a related field # Advanced and in-depth knowledge of electronic structure theory # Strong and highly developed expertise in density
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approaches based on density functional theory (DFT) have been introduced in recent years. A new research theme, "organometallic structures (MOFs)," has been introduced more recently, proposing the use
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density functional to ab initio methods coupled to effective field theory) and related numerical and formal techniques. We are particularly fond of interdisciplinary connections and emerging technologies
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density functional theory (DFT) and many-body perturbation theory (MBPT) based methods, in order to predict photophysical properties of novel semiconductors. The theoretical and computational predictions
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Sung, and Ritesh Ghosh. Applicants with particular research interests in heavy-ion collisions, QCD at finite temperature and density, quantum field theories with strong fields, and neutron-star physics
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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of the group, please visit ORCID: 0000-0002-8591-2652 Research Fields (1) Use density functional theory, many-body perturbation theory, and analytical models to study the novel physical properties of materials
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computational condensed matter physics. We seek a motivated researcher with expertise in density functional theory (DFT) to study emergent phenomena in quantum materials. Research Areas - Correlated electronic
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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
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computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc