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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Driven Autonomous Metrology System Location Physical Measurement Laboratory, Sensor Science Division
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Fundamental Studies of Transduction Phenomena for Microscale and Nanoscale Chemical/Biochemical Sensors NIST only participates in the February and August reviews. Sensors are typically designed
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, P., Wang, J., Li, J. and Cleary, T., 2020. Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire technology, pp.1-22. [2] Wang, J., Tam
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of the constraints on sequencing (read length, depth), and informatics (e.g., database composition, algorithm biases). Proposals should address these challenges with strategies to evaluate the metagenomic
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to work on optimizing MPS designs (potentially with integrated, microfabricated sensors), developing new tissues-on-chips, developing MPS for new organ combinations, and testing drug candidates in
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include multiple laboratories featuring a range of collaborative and non-collaborative robots, industrial autonomous vehicles, mobile robots, mobile manipulators, state-of-the-art sensors, robotic hands
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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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stages, advanced motion control, and instrument system integration. Research opportunities are available in the areas of design and analysis of MEMS sensors and actuators, micro- and nanofabrication, and
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water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these measurements with other techniques including
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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and