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; previous experience as local contact at synchrotron XAS beamlines; knowledge of advance characterization techniques like XANES simulation and ab initio calculations (e.g. DFT). Good time management skills
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Technologien (IWT), Thyssenkrupp Steel Europe AG, Oulun Yliopisto and Ovako Sweden AB, to connect modelling insights with process development and alloy design Contribute to scientific publications, conference
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molecular simulation techniques (density functional theory, molecular dynamics, ab initio molecular dynamics) and in developing machine learning interatomic potentials and can apply these to uncover atomistic
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and machine-learning potentials for planetary materials. Curating and generating large-scale ab initio datasets across wide pressureâ“temperature regimes. Designing and training advanced machine
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background in using the most advanced ab-initio methods to examine electronic, topological and magnetic properties of advanced materials, their defects and their interphases. The incumbent will have
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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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expertise in methods such as machine-learning force-fields for spinful materials, or multi-fidelity Bayesian models that can learn machine-learning force-fields along with effective spin Hamiltonians from ab
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of computational and theoretical chemistry. Research will involve the modeling of molecular materials based on model and ab-initio calculations. Projects will include, but are not limited to, and the topological
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte