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. This research opportunity centers on developing methods, algorithms, data, and tools, to support autonomous experimentation as well as prediction of industry- and/or community-relevant material
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://dx.doi.org/10.1021/acs.jpcb.9b11622 ) as a water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these
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substrate specificities, in next-generation computational metabolomics analysis pipelines. Keywords metabolomics; mass spectrometry; neural networks; algorithms; machine learning; cheminformatics
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and new Monte Carlo or molecular dynamics algorithms for enhanced sampling is essential to bridge length or time scales over many orders of magnitude. Comparison of our simulations with the
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and on novel side chains to allow monitoring of the reconstruction and diagnostic processes. Additional projects may include creating large brain/head virtual data sets using simple growth algorithms