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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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include the porting of lattice QCD libraries to the Exascale Architectures (e.g. Frontier at OLCF) and/or emerging programming models (HIP, SYCL, Kokkos, etc), software optimization and/or algorithmic
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-fidelity modeling, digital twins, generative models, adaptive optimization, or autonomous experiment–simulation workflows. Excellent written and oral communication skills. Motivated self-starter with
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optimization across quantum and classical computing resources. Conduct hardware-software and application-system co-design by considering interactions among quantum hardware characteristics, HPC architectures
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, calibration, and application. Experience with computational methods including steady-state modeling, dynamic simulation, computational fluid dynamics (CFD) to support system design and performance optimization
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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