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Carlo (MC), as well as quantum approaches like Density Functional Theory (DFT). Develop novel Graph Neural Network (GNN) potentials to accurately represent the catalytic behavior of specific species
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density within these films, together with a non-uniform distribution of counterions and specific ion effects but there is no direct evidence supporting this theory. The task of this job is to investigate
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liquid environment. 1) Molecular Modeling - Study of interactions between MnO₂ and ionic liquids using Density Functional Theory (DFT) and Reactive Molecular Dynamics (ReaxFF). - Analysis of oxidation
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on the electronic properties of the investigated heterojunctions. While we will mainly use density functional theory (DFT) to achieve these goals, we will also exploit machine-learning techniques to train more
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chemistry software (e.g., Gaussian, Dalton, TurboMole) is highly desirable. Background in Density Functional Theory (DFT) and Time-Dependent Density Functional Theory (TD-DFT) are desirable assets. Strong
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effects in magnets, this project will develop Density Functional Theory methodology for multicomponent lanthanide materials. We will study, for example, how application of pressure causes the f-electrons in
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, for example via equilibrium and non-equilibrium Green's functions, density functional theory, and tensor network methods. Experience in topological magnetic systems and nonlinear optical phenomena is considered
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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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Experience The GCSC encourages you to present and publish your work early on. Assistance is available through workshops and coaching sessions on academic writing and conference presentations. Our e-journal
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on novel high-resolution microscopy techniques to experimentally quantify transition probabilities, alongside density functional theory (DFT) calculations of defects in feldspar. This inter-disciplinary