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integrated autonomous experimental synthesis and characterization cross-facility agentic-AI platforms that allow real-time guidance and control of these multi-modal experiments for targeted discovery of novel
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languages Hands-on experience with MD simulation tools such as NAMD, GROMACS, AMBER, or LAMMPS, and visualization tools (e.g., VMD, PyMOL) Experience working on high-performance computing (HPC) systems
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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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foundation in machine learning, deep learning, or computer vision Proficiency in Python and experience with ML frameworks such as PyTorch or TensorFlow Demonstrated research productivity (e.g., peer-reviewed
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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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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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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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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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imaging experiments at the MARS beamline (HFIR CG‑1D), including experiment planning, operando support, data reduction, and delivery of quantitative reconstructed datasets Develop and implement scattering
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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