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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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pipelines for large-scale scattering datasets. Participate in synchrotron experiments at APS beamlines to generate datasets that support algorithm development and validation. Work closely with ISAAC
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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energy goals. ESIA also develops, deploy, and advance grid technologies that ensure a robust and secure U.S. grid transmission and distribution system. We collaborate with government agencies as
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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for the successful candidate and illustrates the general nature of the work but is not intended to be exhaustive. In addition, the successful candidate is encouraged to bring their inputs in the research direction
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ability to scale models using distributed computing environments. Excellent oral and written communication skills for effective collaboration across multiple teams. Commitment to embodying the core values
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, distributed systems, or large-scale data pipelines Experience with game engines, XR systems, or advanced graphics frameworks applied to scientific data Record of publications in visualization, AI
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the U.S. achieve energy goals. ESIA develops, deploy, and advances grid technologies that ensures a robust and secure U.S. grid transmission and distribution system. ESIA also collaborates with government
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total X-ray scattering (TXS) and pair distribution function (PDF) analysis capabilities and methodology to study laser-driven structural dynamics in functional materials. This position is part of a