33 pattern-recognition-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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AI/ML surrogate models for inverse design of new materials and processes, incorporating simulated and experimental multi-modal datasets. Develop AI/ML approaches to bridge length- and time-scales in
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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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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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include, but not limited to, one or more of the following: Design and implement system software to enable AI-readiness for scientific data by developing adaptive techniques capable of maintaining
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design and implement
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to accelerate the design and discovery of novel materials. The Materials Theory Group has a background in using first principles methods to examine electronic and thermal transport, magnetic properties
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-based systems Background in topology optimization and structural design Experience in thermomechanical characterization of polymer materials Demonstrated experimental capabilities and a strong
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data center cooling system design is preferred. Flexibility to adapt evolving research scope and programmatic needs. Excellent written and oral communication skills. Motivated self-starter with
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, Research Accelerator Division, Neutron Sciences Directorate at Oak Ridge National Laboratory (ORNL). The successful candidate will work closely with SNS research and operations staff to design and carry
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. Scalability of Preprocessing Pipelines: Design and implement automated, parallel preprocessing workflows capable of handling multi-petabyte datasets efficiently while reducing throughput bottlenecks. Data