319 web-programmer-developer-"https:"-"https:"-"https:"-"https:"-"PhD-Jobs" positions at NIST
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identification of spectral features by computer vision and machine learning. Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular
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polymer networks, with well-defined chemistry and architecture, are needed to carry out quantitative measurements to establish design principles for programmable disentanglement or dissociation of network
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microfluidic networks.Our goal is to develop systems that enable accurate, high-throughput, and dynamic measurement of materials in flow, which will, for example, improve the ability to specify composition and
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are developing high order integral equation methods and numerical tools for computational electromagnetics. This research focuses on the frequency domain electromagnetic field solvers that involve automatic
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We develop and utilize state-of-the-art experimental and computational techniques to acquire, evaluate, and correlate thermodynamic data of standard reference quality with a particular emphasis on
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, are promising emerging manufacturing technologies for producing complex and highly-customized parts. These processes have been in development over the past 15+ years and their capabilities have grown
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. This research aims to develop the label-free chemical imaging method to investigate the microscopic structure and composition of new biomaterials and smart polymers via collaboration with material scientists and
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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
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these complex communities by developing sample preparation techniques that are compatible with NMR and mass spectrometry-based techniques. This will allow parallel multimodal analysis including proteomic
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spectral filters and superconducting detectors on a single chip will enable dramatic new functionality and scalability. We are developing these systems utilizing silicon photonic devices with superconducting