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procedures must be developed. The new hybrid AM processing methods will be instrumented and characterized using a variety of experimental techniques and computational models. key words additive manufacturing
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for critical applications that require qualification and certification—increasingly require that computational models and in-situ monitoring of such processes be experimentally validated under highly controlled
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catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and
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understanding of the physics of the QAHE necessary to design and develop new quantum resistance standards. Additional applications in quantum information science (QIS) can be envisioned for robust QAHE devices
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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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extensive internal and external collaborations, providing access to a full range of state-of-the-art materials characterization and computational modeling capabilities. The results will have broad
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components. To develop this program in oxide electronics, a successful applicant will have a solid background in programming (Matlab, Python, or equivalent). Experience with any of the following lock
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of aqueous chemical models, incorporated into computer codes, for both pure and applied research that include industrial chemistry, chemical engineering, water treatment, hydrometallurgy, toxicology, medical
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to reliable manufacturing of the next generation computing devices. Computational imaging methods such as coherent diffractive imaging, Fourier ptychography, structured illumination techniques, and other super
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of the constraints on sequencing (read length, depth), and informatics (e.g., database composition, algorithm biases). Proposals should address these challenges with strategies to evaluate the metagenomic