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the ionization process and the resulting correlations. Confront the measurements with the ab initio and analytical strong-field theory developed by the UCL partner group, closing the loop between experiment and
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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chemical phenomena using contemporary computational and theoretical methods. We employ and extend ab initio quantum chemistry to understand and predict structure and reactivity in the ground and electronic
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at understanding chemical phenomena using contemporary computational and theoretical methods. We employ and extend ab initio quantum chemistry to understand and predict structure and reactivity in the ground and
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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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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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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