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of compiler- or AI‑generated code. Mathematical Foundations: Background in linear algebra, graph algorithms, and related areas central to dataflow and tensor optimization. Hardware/Software Co‑Design
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, computational physics, computer science, applied mathematics, computational science or a related discipline, earned no more than five years prior to the date of the application Preferred Qualifications: A strong
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. Experience with quantum algorithms (other than variational or adiabatic) and/or quantum hardware Knowledge of topological quantum field theories, topological band theory. Strong analytic mathematical skills
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, and Service. Basic Qualifications: A Ph.D. degree in Applied Mathematics, Computer Science, Physics, Electrical Engineering, or a related discipline. Familiarity with quantum computing theory and
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statistical mechanical theory. Collaborations with internal and external fusion efforts will be strongly encouraged. The successful candidate will be mentored and teamed with staff in the National Center
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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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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications
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, a proven publication record, and effective interpersonal skills. Preferred Qualifications: Knowledge of graph neural networks and other geometric deep learning approaches for graph-structured
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theory, particularly electrodynamics. Experience working with an accelerator, such as in a control room, conducting accelerator-based experiments, or performing accelerator simulations. Experience