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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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Description Developing next-generation joint communcations and sensing applications requires precise measurements of the radio frequency propagation environment as well as sensing target characterization
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mandy.esch@nist.gov 301.975.8575 Description Postdoctoral research opportunities are available for developing tissues-on-chips and multi-organ microphysiological systems (MPS) that realize reliable 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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. The development of specific genome editing technologies leads to the emerging of epigenetic editing, which now allows the epigenetic editing at specific loci and enables direct study of functional relevance
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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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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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are developing machine learning-driven autonomous metrology research systems, with the goal of accelerating the development of self-correcting photonic and quantum sensor networks. These systems combine machine