78 distributed-algorithm-"Meta"-"Meta"-"Meta" positions at Oak Ridge National Laboratory
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discovery. This position is for the Discrete Algorithms group, Mathematics in Computation Section, within the Computing and Computational Sciences Directorate at Oak Ridge National Laboratory (ORNL) and
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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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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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experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will
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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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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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the world’s most challenging problems through artificial intelligence and data driven algorithms and systems. CSMD creates the mathematics, artificial intelligence, and architecture-aware algorithms
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supercomputers, and computational imaging. Research Areas of Interest (include but are not limited to): Vision Transformers and foundation models for scientific and biomedical imaging Federated and distributed
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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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