168 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"LGEF" positions at NIST
Sort by
Refine Your Search
-
devices, coatings, food-related materials, and personal care. Work emphasizes the development of analytical methods for quantitative measurement of engineered nanoparticle properties, including bulk and
-
on the development and application of high-resolution measurement methods to study fundamental problems with broad industrial impact in areas such as the service life prediction of polymeric materials. Recent projects
-
are interested in using Machine Learning and AI techniques to enable autonomous, AI-Driven, experimental research. There are many aspects of this nascent field that require further development. This includes
-
against experiment and by development of reference problems. Important issues include controlling round-off and truncation error to obtain high accuracy solutions in complex, large scale simulations, and
-
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
-
evolution. The Group aims to advance fundamental understanding, improve predictability for design, ensure reproducibility and comparability, and facilitate scalability for real-world applications
-
Michael Pettibone john.pettibone@nist.gov 301.975.5656 Description Detection, characterization and temporal evolution of metal nanoparticles is undergoing environmental transformations. Within
-
the last 2 decades.[1] However, corresponding development of robust and reproducible in vitro assays for evaluating the critical quality attributes and/or the biological responses of these nano-enabled drug
-
communities impact all aspects of the world in which we live, and our relationships with surrounding microbial populations can have negative and positive impacts on the survival of both. The development
-
quantitation of the effects of environmental context and evolution. The Group aims to advance fundamental understanding, improve predictability for design, ensure reproducibility and comparability, and