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-sensor fusion, and propagation modeling — to develop AI-enabled detection, classification, and triangulation algorithms for critical energy infrastructure applications. This position resides within
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-processing, information extraction, and post-processing using existing SDR frameworks and algorithms Rapidly prototype SDR signal chains and systems through design, programming, hardware integration, testing
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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, statistical, probabilistic, or algorithmic solutions to real world problems in the healthcare and biomedical research. As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has
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algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. The MsM group also develops artificial intelligence
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and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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, and performance reports for responsible projects. Develop control algorithms for actuating and controlling mechanical systems. Accountable for compliance with environment, safety, health, and quality in