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Requisition Id 15721 Overview: We are seeking a Postdoctoral Research Associate who will contribute to the development and implementation of novel quantum algorithms for materials simulation, with
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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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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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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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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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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
Requisition Id 15685 Overview: The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to support research directed towards developing novel AI/ML algorithms
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will