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carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory-informed AI-models for creating
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systems (Mojo, Julia, Rust, Python), and HPC system co‑design. This position is embedded within the larger DOE ASCR ecosystem, with direct relevance to ongoing efforts, and related AI‑for‑HPC thrusts
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radiochemical processing, isotope separations, or nuclear systems. Experience with statistical design of experiments and integrated experimental/computational research approaches. Ability to work effectively
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-layer (i.e., large aspect ratio) meshing capabilities. Additional application methods of interest include adaptive meshing for design/shape optimization as well as solution optimization. In
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within the last 5 years. Programming experience including the use of one or more of C/C++, Python, Fortran, Git, and CMake. Writing and communication skills and the ability to publish. Preferred
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relationships via digital manufacturing practices Basic experience with AI/ML techniques Publish research in peer reviewed journals and conferences Support R&D staff members on their projects General support of
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and validating scattering‑correction and calibration workflows that yield quantitative attenuation coefficients, and (2) designing adaptive tomography approaches that reduce acquisition time while
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to accelerate the design and discovery of novel materials. The Materials Theory Group has a background in using first principles methods to examine electronic and thermal transport, magnetic properties
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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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). Major Duties/Responsibilities: Designing, developing, and conducting experiments related to data center thermal management technologies, phase change heat transfer processes, dehumidification systems