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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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, or spectroscopy datasets. Familiarity with total scattering analysis, pair distribution function (PDF) methods, or diffraction data analysis. Experience with automated data pipelines, high-throughput analysis
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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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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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Up-to-date awareness about the status and trend of the computing hardware industry Interest in working with domain experts on practical problems Experience with large-scale distributed systems
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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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, 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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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