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advantage of associated particle separations, physical characterization, and chemical analysis. Projects incorporating machine learning and chemometric approaches are also welcome. We are seeking independent
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of solvation, redox potentials, pKa, spectroscopic observables, enzyme kinetics, etc) for these processes provide a rigorous framework for the validation of novel computational methods. Computational methods
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correlations and prediction methods. The program will build on our existing efforts using Quantitative Structure-Property Relationship (QSPR) methodologies and modern machine learning methods (support vector
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the work of collaborators engaged in experimental studies of the additive process itself and in detailed characterization of the microstructures, residual stresses, and ultimate properties using synchrotron
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RAP opportunity at National Institute of Standards and Technology NIST Advanced X-ray Fundamental Parameters Location Physical Measurement Laboratory, Radiation Physics Division opportunity
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RAP opportunity at National Institute of Standards and Technology NIST Label-free Optical Medical Imaging Location Physical Measurement Laboratory, Applied Physics Division opportunity location
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on the initial crystallographic texture and uniaxial stress-strain data, thereby predicting the evolution of the yield surface in multi-axial tensile space for a real specimen. Computed constitutive models will be
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measurement platforms using present and next generation electron microscopes. If you are a creative individual and can imagine what “can be” given the data richness of our program, we invite you to apply and
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bioassays to measure variations found within biofilms and to determine the effects of the underlying substratum on the biofilm chemical and physical structure. In addition, biofilms can be composed
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301.975.6662 Description An experimental and modeling program is underway to further the understanding of dynamic processes that occur in fires and to reduce the impact of fire on people, property, and the