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of organic chemistry, molecular structure, and/or drug-discovery principles. Demonstrated interest in applying machine learning or computational methods to chemical or biological problems. Motivation to work
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency
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for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice