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further enriches the available data from which material behavior can be extracted. Separate work is being done to develop robust algorithms to quantitatively compare the physical and simulated experimental
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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
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to acquire different kinds of images on large numbers of iPS cells in culture; machine learning algorithms and other image analysis strategies may be used to extract and test image features as predictors
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particle dynamics (CFPD), a fluid-particulate coupled algorithm, to (i) quantify breath device operation (i.e., species deposition, complex fluid flow) [1], (ii) guide experimental breath species collection
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algorithms for enhanced sampling is essential to bridge length or time scales over many orders of magnitude. Comparison of our simulations with the experimental results of others [e.g., small-angle scattering
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Reconstruction Algorithms,” ICASSP 2015. (4) D.M. Pelt and J.A. Sethian, “A mixed-scale dense convolutional neural network for image analysis,” PNAS, January 8, 2019. If interested then, please, contact: Peter
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metabolomics analysis pipelines. key words metabolomics; mass spectrometry; neural networks; algorithms; machine learning; cheminformatics; biostatistics; bioinformatics; big data Eligibility citizenship Open to
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volume and quality that is consistent with the use of statistical methods; machine learning techniques for knowledge discovery; protein-protein interaction network analysis; novel algorithms for next
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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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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form