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informatics developers in the public-private-academic Genome in a Bottle Consortium to develop methods to integrate short-, linked-, and long-read sequencing technologies to form benchmarks for somatic variant
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increase throughput and provide rich datasets that can be exploited by machine learning and artificial intelligence. Current advanced mechanical testing activities involve three-dimensional surface digital
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involving an actual or planned nuclear attack. Conclusions drawn from this collected data coupled with law enforcement and intelligence information may support attribution—the identification of those
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are essential for broad adoption of these methods, this postdoc would collaborate with a unique array of technology and informatics developers in the Genome in a Bottle Consortium to develop authoritative de novo
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multiple 2D images and multiple channels, (d) optimizing 2D projection viewpoints (dose reduction and time savings), (e) applying artificial intelligence and traditional machine learning models to noise
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these models do not account for realistic conditions and require lengthy computational time. In order to overcome the practical challenges and numerical bottlenecks, the Fire Research Division of NIST’s
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intelligence and machine learning tools. 1. S. M. Chavali, J. Roller, M. Dagenais and B. H. Hamadani, Sol. Eng. Mater. Sol. Cells, 236, 111543 (2022). 2. B. H. Hamadani, Appl. Phys. Lett., 117, 043904 (2020
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for data-driven (machine learning / artificial intelligence) applications. References Hoogerheide, D. P. et al. Structural features and lipid binding domain of tubulin on biomimetic mitochondrial membranes
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energetics, thermodynamics, kinetics of protein-DNA interactions, and related phenomena, and connecting these measurements to organism fitness. Computation for engineering biology, such as RNA circuits, in