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be generating terabytes of image data per day, there are several open research problems that would become the research focus of a postdoctoral candidate. The problems include (a) finding optimal
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our understanding of the fundamental limitations of detectors and sources; development of new ways to package detectors, sources, and components optimized for few photon operation; and developing new
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learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods
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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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to work on optimizing MPS designs (potentially with integrated, microfabricated sensors), developing new tissues-on-chips, developing MPS for new organ combinations, and testing drug candidates in
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NIST only participates in the February and August reviews. In many application areas, materials development increasingly involves manipulating the local atomic order to optimize properties
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experimental data archives available at NIST/TRC and advanced multidimensional optimization techniques to generate self-consistent, predictive force-filed models applicable over wide range of conditions. key
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are designed, constructed and operated to provide optimal spaces for living and working. The industry needs tools, metrics, and processes to create buildings that are more than just static structures but living
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, heterogeneous metastable microstructures, and nonequilibrium phases. These challenges make it difficult to establish the critical structure-process-performance relationships required for qualification and