320 web-programmer-developer-"https:"-"https:"-"https:"-"https:"-"Fraunhofer-Gesellschaft" positions at NIST
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determination of marijuana components, development of vapor measurement technology and canine training aid materials for opioids and improvised explosives, targeted and non-targeted screening of bulk samples and
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prevent a true function-by-design approach to development and manufacturing. We are interested in using analytical theory, large-scale molecular dynamics (MD) simulations, and density functional theory (DFT
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correlations were traditionally developed on the basis of some reliable but often very limited data compilations. Currently, large comprehensive experimental data collections have not only become more readily
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trade-offs that severely compromise the overall efficiency and lifecycle of flow battery technology. We are working to develop strategies to improve on those performance trade offs. We are using rheology
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course-grained simulations of nanotubes with large adsorbed dispersant molecules in solution. It is expected that the challenging nature of these simulations will require the development of novel
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the world. With this production system, we are looking to augment our ability to rapidly answer science questions using the aggregated data volume. Additionally, we seek to develop and deploy new autonomous
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al, 2019) provides a facile route to explore such chemistry. This research opportunity aims at applying photo- and redox-chemistry of DNA to obtain programmable covalent surface modification of SWCNTs
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spin dynamic simulations are available to develop approaches compatible with industrial environments. Applications in topic areas can be broad reaching with examples (but not limited to): energy
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(1) development of nanoscale characterization techniques to measure mechanical, chemical, and rheological properties of microscopic volume elements with nanoscale spatial resolution using atomic force
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are not sufficiently accurate, or the methods are too expensive to accurately model sufficiently large systems. As a result, these computational problems are ideal for developing machine-learned potentials