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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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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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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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publications, conference presentations) Experience designing experiments, evaluating models, and analyzing results Ability to work effectively in interdisciplinary, team-based environments Preferred
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) and quantum physics Familiar with programming languages and scientific computing Preferred Qualifications: Experience working with low-rank linear algebraic approximation methods. Experience with
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a closely related field completed within the last 5 years A track record in the physics of correlated oxides or 2D materials Experience in thin film deposition or 2D materials synthesis Preferred
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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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and validating scattering‑correction and calibration workflows that yield quantitative attenuation coefficients, and (2) designing adaptive tomography approaches that reduce acquisition time while