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continues to push patterning to new limits. There are significant needs to understand how the components in these resists are distributed, and critically whether there is aggregation that could contribute
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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
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algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify the presence of a peptide using different
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pluripotent stem cell lines from the same individuals, could serve as reference samples for other ‘omics technologies as well. For example, this postdoc could take advantage of the extensive single molecule
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systems. This work will specifically focus on combining ML algorithms with classical data analysis and control techniques to develop robust in situ (i.e., in real-time, during the operating experiment
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, economics, and all branches of science. Current concerns include the development and analysis of algorithms for the solution of problems of estimation, simulation and control of complex systems, and their
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-eddy simulation and direct numerical simulation of the phenomena. Topics of interest include algorithm development numerical combustion, scientific visualization, and data analysis. key words Buoyancy
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, this project will employ emerging proteomics techniques (such as data-independent acquisition) and will be working alongside software and algorithm developers to ensure that these platforms can be used beyond
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Computational electromagnetics; Integral equations; Numerical algorithms; Fast multipole method; Field solvers; Eligibility citizenship Open to U.S. citizens level Open to Postdoctoral applicants Stipend Base
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materials. We are interested in studying the structure and mechanical properties of polymer networks with defined molecular topologies (functionality, branching, and molecular mass distribution), as