79 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at NIST in United States
Sort by
Refine Your Search
-
Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
-
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
-
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...
-
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
-
are interested in using Machine Learning and AI techniques to enable autonomous, AI-Driven, experimental research. There are many aspects of this nascent field that require further development. This includes
-
. By leveraging material simulation and Machine Learning Interatomic Potentials (MLIPs), we aim to accelerate the interpretation of inelastic (INS) and quasi-elastic neutron scattering (QENS) data
-
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
-
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
-
Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
-
materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling