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control, and model retraining, enabling reproducible, scalable, and high-reliability AI-driven experimentation. Report and publish scientific results in peer-reviewed journals in a timely manner Present
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). Major Duties/Responsibilities: Develop AI/ML models and systems for diverse data and mission contexts Design and implement reproducible pipelines for data acquisition, feature engineering, model training
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retraining strategies. Familiarity with emerging trends in data-centric AI, model-driven data engineering, and federated learning. Contributions to open-source data engineering, ML Ops, or data science
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group of scientists, technicians, and engineers on challenging, application driven projects in support of the DOE Isotope Program and other DOE mission areas. This has been a quickly expanding program at
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framework for driven and open quantum systems. Phenomenological modeling of dynamics/transport behaviors in complex systems, including strongly correlated electron systems. Experience in analyzing data from
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development, business model transition (from support to mission-driven), and growing a team into a standalone organizational entity. Excellent written and oral communication skills. All team members deliver
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Requisition Id 15585 Overview: The Modern Nuclear Instrumentation and Controls (MNIC) Group of the Advanced Reactor Engineering and Development (ARED) Section in the Nuclear Energy and Fuel Cycle
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exploiting multiple diagnostics, data-driven/ML/AI analysis techniques, and pellet fueling modeling validation. Support and upgrade ORNL diagnostic and pellet injector hardware located at W7-X such as
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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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breakthrough science in fields like fusion energy, climate modeling, AI, and national security. Collaborate with diverse teams of scientists, engineers, and technologists from across the DOE complex and academia