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related definitions. Knowledge of SOTA federated learning algorithms. Knowledge of distributed optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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algorithms, capable of distributed learning on high performance and edge computing; The design of architectures/models which accurately capture the complexities of the data, with robust estimates of confidence
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resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's computational resources, including the Frontier supercomputer
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distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and
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algorithms and parallel/distributed variational algorithms in AI/ML for application workflows and large-scale HPC and QC systems Develop quantum machine learning (QML) algorithms for optimization of multi
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Requisition Id 15281 Overview: We are seeking a Postdoctoral Research Associate who will focus on distributed sensing using optical fibers for maintenance and component health applications
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geometries and advancing to multimodal damage assessment capabilities. The group conducts cutting edge research and publishes on novel ML breakthrough algorithms for large scale geospatial application
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timing distribution. Experience with PCB schematic capture & layout tools such as Cadence, Mentor Graphics, Altium, or KiCAD. Experience implementing DSP algorithms on FPGAs. Experience with Vivado High
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component