Sort by
Refine Your Search
-
. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position
-
advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics
-
that can be integrated into the research workflows used in developing new materials (e.g., carbon nanotubes) or in determining disease pathologies (e.g., Alzheimer’s disease). We want to explore solutions
-
advance the various existing commercial and research technologies that are currently (or have future potential to be) employed for IPM of industrial metal AM machines, and developing new methods
-
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
-
areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
-
resolution techniques are explored to achieve quantitative reconstruction of nanoscale structure images by developing novel DUV/EUV imaging optics and quantitative phase retrieval algorithms. A qualified
-
metabolomics. Our studies focus on developing new mass spectral data analysis algorithms (e.g., clustering) to better solve the common key persistent problems arising from factors such as mass shift and peak
-
and then accessed by a team of experts. We are seeking candidates to address these challenges that range from algorithm development, simulation of reference data, algorithmic accuracy evaluations, design of
-
NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form