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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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resolution techniques are explored to achieve quantitative reconstruction of nanoscale structure images by developing novel DUV/EUV imaging optics and quantitative phase retrieval algorithms. A qualified
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are needed to support the clinical testing community and manufacturers of working standard materials. Challenges lie in developing relevant standards in a timely fashion to support new clinical targets and
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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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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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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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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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. 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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, 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