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Description We are seeking a NIST postdoctoral Fellow within the Materials Measurement Science Division . This postdoc will be a key member of a new project to develop an autonomous platform for developing and
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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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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 sequencing data and other
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are interested in postdoc candidates with prior experience in any of the following areas: ceramic processing, nano-particle synthesis, colloidal chemistry (i.e., rheological testing, zeta-potential), advanced
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for metabolomics studies in human health and precision medicine applications as well as food and nutrition, biomanufacturing, and forensic science, including liquid or gas chromatography and/or ambient ionization
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, nutritional/dietary, and pharmaceutical science. Sorbent materials currently under investigation include perfluorocarbons, templated alkylsilanes, novel polymers, and mixed-mode phases bonded to metal oxide
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This research focuses on developing new applications of high resolution/accurate mass (HRAM) mass spectrometry for environmental, forensic, and nutritional analyses. HRAM mass spectrometry instrumentation has
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are essential for broad adoption of these methods, this postdoc would collaborate with a unique array of technology and informatics developers in the Genome in a Bottle Consortium to develop authoritative de novo
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, 2016 , and Microphysiol Syst. 2020 Jun; 4: 10.21037/mps-19-8 ) that will make toxic metabolite detection more sensitive. Recent work includes a new, pumpless liver/heart system. NRC postdocs can propose
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. A key component of the project is high sensitivity magnetic scanning microscopy of these nano-objects, most likely using SQUID magnetometers. We are seeking a postdoc with background in scanning