70 machine-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at NIST
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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
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for the world. Likewise, U.S. manufacturing depends on its 500,000+ machine tools that make precision parts. However, a major problem with these machines is that their performance, which degrades over time due
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local disorder as probed by X-ray Absorption Spectroscopy result in emergent material properties Machine-learning driven electrochemical deposition of coatings controlling surface properties
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Advancing the state of the art in measurements of sound, vibration, force, acceleration and velocity
information into neural networks for modeling dynamic systems; use of uncertainty information to improve the performance of sensor-network based measurements employing machine learning; uncertainty
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Semiconductor manufacturing is a complex procedure with challenging variations in machines and processes. For example, due to high-mix semiconductor manufacturing, in which hundreds of types of products
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Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
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are developing microfluidics to measure material properties and structure. Protein, polymer and surfactant solutions and suspensions and emulsions are being characterized using computer-controlled microfluidic
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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
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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
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. 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