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a PhD in Mechanical Engineering, Materials Science or Physics who has expertise in multi-physics simulations, non-linear FEM, inverse analysis and constitutive modeling. Prior experiences in modeling
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digital models through an additive process. AM enables the rapid production of complex parts with minimal lead time, fewer constraints, and reduced assembly requirements. This makes it an attractive option
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Semiconductor manufacturing is a complex procedure with challenging variations in machines and processes. For example, due to high-mix semiconductor manufacturing, in which hundreds of types of products
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phases. This research will integrate a variety of modeling tools across multiple time and length scales to predict the microstructure evolution during the AM build process and post build thermal
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structure, crystallographic phase, grain structure, crystallographic texture, crystal defects, lattice strain, contaminants • Imaging of 3D assemblies of 2D materials: moving toward characterization in
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measurement techniques and models for the thermophysical and interfacial properties of process streams; and the application of dielectric acoustic techniques to determine the thermophysical properties
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to this process is the understanding of phase relations, phase transitions, processing-structure, and structure-property relationships. Equally important to the modeling efforts are the experimental
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. Chemical engineers constantly need reliable property data for process design development and optimization. This information is predominantly coming from scientific publications. Thousands of papers
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processes that we cannot measure with off the shelf instrumentation. This NRC fellowship will explore the emerging field of acoustofluidics and its possible applications on acoustic absorption spectroscopy
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an understanding of the basic properties of atom-based solid-state and nanoscale systems, optics on the nanoscale (nano-optics) quantum processes in atom-scale and nanoscale systems with optical fields, and quantum