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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data
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Description Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and
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and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's
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Research. We are seeking applications from researchers in the broad domain of computational and applied mathematics, including algorithm analysis, artificial intelligence, combinatorial scientific computing