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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering Division (CSED
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) and hyperparameter optimization or AutoML techniques. Proficiency in Python and familiarity with software engineering best practices (version control, testing, documentation). Experience with HPC
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/or machining process optimization Hands-on experience with CNC machine tools, machine controllers, G-code, CAM software, machining process planning, fixture design, and practical manufacturing
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The candidate is expected
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LiDAR, IMU, camera, and wheel-odometry data in GPS-denied, low-light environments. Implement LiDAR-based or LiDAR-inertial SLAM, factor-graph or pose-graph optimization, loop-closure validation, drift
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral