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validation. Experience or strong interest in scientific AI/ML, including surrogate or multi-fidelity modeling, digital twins, generative models, adaptive optimization, or autonomous experiment–simulation
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expertise in multi-physics FE simulations is preferred. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process
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interfaces and protocols, such as Address-Event Representation (AER). Experience with hardware-aware AI optimization, including quantization, pruning, fixed-point arithmetic, and memory-footprint
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optimization, loop-closure validation, drift estimation, and drift-correction methods for long trajectories in environments with limited and repetitive geometric features. Develop autonomous navigation
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), software optimization and/or algorithmic research and development in the areas of gauge field generation, linear and eigen-solvers, or novel analysis methods. This position will reside in the Advanced
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management, execution orchestration, and optimization across quantum and classical computing resources. Conduct hardware-software and application-system co-design by considering interactions among