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analysis (FEA)—to evaluate and predict the behavior of complex rotating machinery operating under extreme conditions. Through the integration of modeling, simulation, and experimental validation, the group
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with our partners and sponsors. Research activities include, but are not limited to: Rapid prototyping and evaluation of experimental systems, including hardware, software, tools, and applications, and
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experimental validation of high-performance polymer composite systems. This position resides in the Composites Innovation Group in the Manufacturing Science Division, Energy Science and Technology Directorate
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breakthroughs. The CSM Division delivers fundamental and applied research capabilities in a wide range of areas, including applied mathematics and computer science, experimental computing systems, scalable
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. This section will advance the integration of high‑performance computing (HPC), artificial intelligence (AI), data science, and automation with experimental biosciences to enable predictive, scalable, and AI
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is to integrate computational modeling with experimental methods and provide innovative solutions for technology growth. We aspire to help generate technical artifacts (patents) as well as scientific
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-based systems Background in topology optimization and structural design Experience in thermomechanical characterization of polymer materials Demonstrated experimental capabilities and a strong
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, supported by: Multiscale modeling (material (molecular) → process → manufacturing (scale up)) Data-informed experimentation Selective use of AI/ML and big-data techniques where they add real value
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characterize radiated emissions from transmitters of interest Provide feedback on signal quality/attributes of collected data to support hardware development efforts Support planning of experimental campaigns
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including electromagnetic isotope separation Participate in research activities utilizing Operating Instructions, Experimental Plans, or other required documentation Assembly, disassembly, and troubleshooting