Sort by
Refine Your Search
-
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
-
RAP opportunity at National Institute of Standards and Technology NIST Incorporating Theory and Domain Knowledge into the Machine Learning of Polymeric Systems Location Material Measurement
-
NIST only participates in the February and August reviews. The NIST Electron Microscopy Nexus is an internal shared-use facility with 13 electron microscopes (S/TEM, SEM, dual-beam instruments), and our data infrastructure is set up so that all microscopy datasets from all users are collected...
-
Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
-
polyethylene and polypropylene. To overcome this challenge, we have previously shown that utilizing machine learning, real-time NIR measurements can be correlated to molecular architecture sensitive properties
-
DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
-
consideration will be made to candidates with experience in automation or machine learning. The postdoc will join a group which is focused on pioneering applications of modern machine learning methods, FAIR data
-
301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales
-
manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and their processes. Semiconductor manufacturers desire
-
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