Sort by
Refine Your Search
-
plasticity, where genetic drift, transgene instability, or chromosomal rearrangements can alter product quality or yield over time. Understanding this genomic evolution is essential for assuring
-
on the science that will underpin the development of the needed metrology to close this gap. The ideal candidates would have some understanding of high frequency electrical characterization, as well as substantial
-
NIST only participates in the February and August reviews. This opportunity focuses on the development and implementation of liquid chromatography mass spectrometry methods for the quantitation
-
physical sensing, quantum science, communications, and dynamic spectroscopy. We have developed novel approaches to comb generation [1], spectral translation [2], and their use to interrogate cavity
-
, acoustic-electric spectroscopy, and other nonlinear materials characterization techniques. We will develop on-wafer acoustic microfluidic devices. Necessary skills include finite element simulations, digital
-
causes of data variability to improve product quality and reproducibility [1]. Simulation Modeling: Developing theoretical and mathematical descriptions of physical phenomena, including both physics-based
-
, plays an important role at NIST in the development and interpretation of new measurement techniques, as well as aiding the understanding of the behavior of new materials in existing measurements. In
-
, (2) interpretation of experimental spectra, (3) development of semi-empirical methods, (4) studies of reactivity indices, (5) computational electrochemistry, and (6) chemical informatics. The explosion
-
provides the thermochemical foundation for new noninvasive breath analysis techniques. Law enforcement applications include the development of breath analysis devices for the quantitative measurement of drug
-
Consortium led to the development of the first NIST RMs in this class, with widely-used benchmark germline variant calls for seven human cell lines [1]. Artificial intelligence and machine learning hold