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, and cyber-resilient operation of distribution systems and networked microgrids. The successful candidate will contribute primarily to the control and cybersecurity thrusts of a multi-institutional
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reinforcement learning Experience with high-performance computing, physics-based simulations, and multimodal data workflows Demonstrated ability to train and deploy AI/ML models using simulated and experimental
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instrument proposed under a DOE Major Item of Equipment (MIE) effort. Building on two decades of APS XRS capability (including the LERIX program at 20-ID) and recent commissioning work at Sector 25
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-Informed Neural Networks (PINNs) and geometric deep learning. Experience with active learning, agentic workflows, or other methods for autonomous experimentation. Familiarity with high-performance computing
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of progress in relevant fields and contribute to CNM and the broader user community through method development, best practices, and mentoring Support end-users with high performance computing and workflow needs
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(CFD) to develop and optimize new processes and equipment designs using high-performance computing Develop process- and facility-scale models as the foundation for digital twins of chemical processing
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in data management and high-performance computing, including workflow design and optimization Strong oral and written communication skills, with the ability to work effectively with internal and
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. Experience in numerical methods and CFD development using mesh-based scientific codes. Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications High performance computing (HPC
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++, or similar languages. Demonstrated expertise in machine learning, especially in the context of dynamical systems modeled by differential-algebraic equations. Experience with high-performance computing and the
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, and engineers specializing in electron and X-ray microscopy, fostering a collaborative, inclusive, and high-performance culture Advance the CNM user science program, enabling cutting-edge experiments in