Sort by
Refine Your Search
-
processing of tables, extraction of property data from plots, analysis of paper content and extraction of metadata (substances, description of their samples, experimental methods, uncertainties, etc
-
and development of gene therapies. This NIST postdoctoral research opportunity focuses on developing robust protocols and refining measurement methods in infectious titer assays. Activities can include
-
material property data, as defined by a pre-determined constitutive model, using inverse methods. Microstructural characterization using SEM, TEM, x-ray, and neutron scattering is applied when appropriate
-
. Nondestructive methods such as Prompt Gamma Activation Analysis (PGAA) are well suited for multi-elemental analysis for bulk materials. The research will explore imaging of gamma ray emission by Compton scattering
-
on a collaboration with experts across multiple Laboratories at NIST involving detector-response modelling, next-generation TES sensor design, and quantitative sample-preparation methods. key words
-
spectrum. A theoretical approach must include automatic discovery of reactions and their rates. Some tandem methods (CID, IRMPD) may be modeled as occurring on the ground electronic state. Electron
-
. This is especially critical for identifying new (potential) pathogens such as the outbreak of a novel E. coli strain O104:H4. However, this can be difficult using the existing platforms and software because
-
. To embrace these changes, this project will develop a shared calibration service, where NIST and the customer share broadband integrated-circuit calibration artifacts, protocols, software, and data through
-
NIST only participates in the February and August reviews. Self-assembly methods have the potential to integrate heterogeneous nanoscale objects to create multifunctional systems, with applications
-
new measurement capabilities, standards, or applications or improve existing methods for reference artifact calibration. For more information, see https://www.nist.gov/pml/sensor-science/dimensional