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candidate will support research and development projects that advance the state of the art in machining science, machine tool design and characterization, manufacturing process optimization, and digital
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disciplines. Hands-on experience designing, synthesizing, fabricating, characterizing, and evaluating materials for separation applications. Research experience in one or more of the following areas: membrane
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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characterization of inorganic sorbents/membranes/solvent extraction for critical mineral extraction from brines, minerals, and coal ash. This position resides in the Nanomaterials Chemistry Group in the Separations
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fabrication, instrumentation, experimental setup development, and advanced characterization techniques. An excellent record of productive and creative research as demonstrated by publications in peer-reviewed
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characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems. Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering Division (CSED
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, characterize physiological impacts, and link genotypes to phenotypes using next‑generation sequencing, systems biology, and complementary analytical approaches. This position requires close collaboration with
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, you will closely collaborate with a team that includes condensed matter theorists, experts in neutron/X-ray scattering, and experts in thin film and single crystal materials growth and characterization