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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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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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Understanding of multidimensional and tabular modelling, vector databases, Graph DB Experience using Microsoft Visual Studio or Visual Studio Code, Python, PyTorch, TensorFlow Two to three years of experience
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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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to infrastructure vulnerability, interdependency, disruptions, restoration, and community impacts. Apply machine learning, deep learning, natural language processing, graph learning, benchmarking, and evaluation
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clearance, DoD TS with SCI and Poly Understanding of multidimensional and tabular modelling, vector databases, Graph DB. Experience using Microsoft Visual Studio or Visual Studio Code, Python, PyTorch
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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