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, and algorithm design for inferring conclusions from multiple sources of information. Uncertainty quantification and propagation is vitally important such autonomous workflows, as is the development
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and then accessed by a team of experts. We are seeking candidates to address these challenges that range from algorithm development, simulation of reference data, algorithmic accuracy evaluations, design of
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
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-deconvolution algorithms that can account for peak asymmetry due to imperfect shims; the use of spatially selective or multidimensional NMR methods; and the development of reference materials, especially for gas