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maturation, characterizing performance and properties of nuclear fuels and materials, and generate the data to advance physical modeling and simulation. The primary function of this open position is to perform
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. Construct machine-learning models for feature-based molecular property prediction and drive the inverse design of ligands with engineered properties. Develop machine-learned interatomic potentials trained
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photo-bases. The work will focus on modeling of adiabatic and nonadiabatic photochemical processes to capture excited states dynamics using an array of ab initio molecular dynamics methods for excited
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