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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability
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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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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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molecular motors and RNA-folding. In a parallel effort, we significantly enhanced the biophysical capabilities of an atomic force microscope (AFM). Specifically, we achieved sub-pN force precision and
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parallel, low-cost analysis systems that do not rely on optical or aptamer-based labels. Before such systems can be realized, the electromagnetic response of biochemical samples must be understood in detail
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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
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are developing object-oriented computational tools for the analysis of materials with complex microstructures. Starting from a digitized micrograph, the program identifies features in the image, assigns material
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measurement to support a DARPA program on “Tailorable Composite Feedstock and Forming”. This project will involve dc to 110 GHz complex permittivity and permeability characterization with on-wafer techniques
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mass spectrometry; and advanced chemometric tools for the analysis, interpretation, and comparison of complex metabolomic data sets. https://www.nist.gov/programs-projects/metabolomics-quality-assurance
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of LC-MS patterns of biological fluids. Previous knowledge and experience of a high-level programming language--preferably C++, Python, or Java and of pattern recognition techniques--to draw statistically