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scientific imaging. The primary responsibility of this position is designing and implementing sparse algorithms for large-scale scientific and numerical computations. The successful candidate will pursue
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distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
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., neutron imaging, SPRUCE hydraulic/thermal thresholds and scaling Produce and publish AI-ready datasets to the ESS-DIVE data archive and BER data lakehouse Develop AI pipelines for experimental ecophysiology
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