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of experimental datasets with modeling and simulation frameworks Dynamic simulation and digital twins for industrial chemical processes Process optimization and model-based decision support tools Development
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processes Development of phenomenological, thermodynamic, and kinetic models for complex reactive systems Integration of experimental datasets with modeling and simulation frameworks Dynamic simulation and
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computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and
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Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning