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, participating actively in group discussions, and potentially contributing to undergraduate or graduate teaching. Applicants should hold (or be close to completing) a PhD in computational/theoretical chemistry
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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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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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The laboratories of Dr. Jette Lengefeld (www.lengefeldlab.com ) and Dr. Nahid Khan are inviting applicants for two post-doctoral and two PhD student positions in stem cell, metabolism and aging
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of chemically storing and releasing hydrogen, using methanol as a reservoir. Main activities: • Utilize global optimization codes and perform DFT calculations on supercomputers. • Analyze results and
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knowledge of electronic structure theory and a strong foundational understanding of solid-state physics. Experience with Density Functional Theory (DFT) and a high interest in an academic career. Strong
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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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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 2 months ago
(molecular dynamics, DFT, and coupled cluster methods). CAS hosts experimentalists, observers, and theoreticians who investigate together the chemical and physical evolution of the interstellar medium and star
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vibrations), and structural (migration of atoms) effects with an atomistic resolution. This can be achieved by self-consistently coupling molecular dynamics (MD), density-functional theory (DFT), and quantum
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform