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to keep the data up-to data with very little effort. Estimate energy and land needs for the realization of the CCU potential. Develop a multi-objective optimization model for individual CCUS projects
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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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independent postdoctoral fellows with training and expertise in agent-based modeling, neural networks, social network analysis, machine learning, data analytics, computational economics, or computational social