364 web-programmer-developer "https:" "https:" "https:" "https:" "https:" "Newcastle University" positions at NIST in United States
Sort by
Refine Your Search
-
. This problem becomes even more pressing for simultaneous multi-qubit operations. The goal of this project is to develop software tools for the automated tuning of high-fidelity readout and gates in silicon spin
-
, 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
-
the field of flexible electronics. Developing an effective flexible electronic structure has its own challenges from mechanical compliance of the substrate to device performance. There is a delicate balance
-
exist for development of theory for and measurements of background and critical region thermal transport properties of such mixture systems. Proposals that integrate theoretical development with
-
@nist.gov 301.975.2860 Description New developments in detector technology have made possible the acquisition of the full electron scattering distribution at each pixel in a scanning transmission electron
-
property data primarily intended for model development that investigate how the molecular size, molecular structure, and polarity of fuel constituents impacts their thermophysical properties. Measurements
-
. Advisers name email phone Yamil Simon ysimon@nist.gov 301.975.8638 Description NIST has long developed and provided reference materials to assist others in making reliable measurements. The NIST Standard
-
. Chemical engineers constantly need reliable property data for process design development and optimization. This information is predominantly coming from scientific publications. Thousands of papers
-
thomas.forbes@nist.gov 301.975.2111 Edward Ryan Sisco edward.sisco@nist.gov 301 975 2093 Description This opportunity focuses on developing and measuring the capabilities of ambient ionization mass spectrometry
-
DeCost brian.decost@nist.gov 301.975.5160 Description Trustability and physical interpretability are critical requirements for the development of robust and sustainable machine learning systems needed