Sort by
Refine Your Search
-
and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
-
of novel optical methods for nanoscale dimensional measurements using the NIST 193 nm Microscope: a newly upgraded, custom-built, world-class high-magnification optical imaging platform optimized
-
NIST only participates in the February and August reviews. The Applied Economics Office (AEO) at NIST works closely with the NIST Community Resilience Program (CRP) and external collaborators
-
Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
-
://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
-
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 Computational Studies of Functional Oxide Materials and Devices Location Material Measurement Laboratory, Materials
-
Computational Mathematics Division opportunity location 50.77.11.B7430 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Vladimir V Marbukh marbukh@nist.gov
-
Computational Mathematics Division opportunity location 50.77.11.C0256 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Paul Nathan Patrone paul.patrone
-
increasingly clear that Machine Learning/AI are having great impacts across a number of fields of physics. This research opportunity revolves around applying these techniques towards optimizing experimental