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Field
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, Density Functional Theory, Molecular Dynamics, Monte Carlo, machine learning, phase field simulations
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. Major duties include: Calculate interaction energies from density-function theory Train machine learning models/potentials Conduct atomistic simulations to explore adsorption/diffusion Analyze
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mechanics, solid-state physics/chemistry, and/or quantum mechanics • Practical experience with density functional theory calculations (e.g., VASP, Quantum Espresso, Q-Chem, PySCF) • Proficiency in
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of spectroscopic measurements. Investigate underlying receptor-analyte interactions with the aid of in silico calculations, including density functional theory (DFT) simulations. Apply machine learning approaches
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engineering, or a closely related field, in hand by the date of appointment. ● Research experience performing first-principles electronic structure or density functional theory calculations using
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, the work proposed for this postdoctoral position aims to understand how intraspecific and interspecific interactions combine to shape the dynamics of metacommunities and ecosystems, via the processes
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materials, organic solids, molecular materials, or other solid-state systems. Experience with computational approaches relevant to structural characterization, including density functional theory (DFT
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particular emphasis on time-dependent density-functional theory (TDDFT). Understanding how electrons evolve in time is central to modern science and technology, from photochemistry and catalysis to quantum
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compositionally complex recycled steels, using density functional theory and machine-learned interatomic potentials, in close collaboration with leading academic partners and Tata Steel. Job description At TU Delft
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rather than essential, and we encourage you to highlight any relevant experience in your application: Single crystal diffraction and advanced diffraction methods. Density functional theory calculations