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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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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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density-functional-theory (DFT) accurate large scale atomistic simulations of defects including dislocations, grain boundaries and precipitates, as well as phase diagrams exploration. A key challenge faced
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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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receiving guidance and support. In the role you will: Learn and use symmetry analysis as a basis for predictions Perform density functional theory (DFT) calculations using VASP, focusing on surface and
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energies will be measured through threshold collision-induced dissociation (T-CID) experiments, with tandem MS/MS experiments used to ensure accuracy. Computational methods like Density Functional Theory
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energies will be measured through threshold collision-induced dissociation (T-CID) experiments, with tandem MS/MS experiments used to ensure accuracy. Computational methods like Density Functional Theory
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experimental investigation for battery development, both supported by the Icelandic Research Fund (RANNIS), a project grant for 3 years. Main tasks The first project involves density functional theory (DFT
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(CFD) and density functional theory (DFT) forms a powerful in silico approach to understanding CVD at the atomic level, while kinetic Monte Carlo methods can be used to study the development of different