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Machine Learning for High Throughput Materials Discovery and Optimization Applications NIST only participates in the February and August reviews. We are developing machine learning algorithms
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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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internationally leading programs that apply the tools of chemical biology or medicinal chemistry (e.g. structure-based methods for drug discovery, combinatorial chemistry, hit-to-lead optimization, drug development
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Change, Glaciology, Modeling, Imaging, Algorithms and Combinatorial Optimizations. Eligibility to hold scholarship funding: Recipients must be offered and accept admission to a UBC PhD program in
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the European Union, a certificate of equivalence (NARIC) must be submitted. You have experience in at least one of the following: o Mathematical modelling of combinatorial optimization problems o Implementing solution