224 parallel-processing "https:" positions at Oak Ridge National Laboratory in United States
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Requisition Id 15813 Overview: We are seeking a highly motivated postdoctoral researcher with a strong background in sensor integration, data acquisition, and in situ process monitoring
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transformative solutions to compelling problems in energy and security. The Enrichment Systems Engineering Section is seeking a Process Design Engineer who will support the Enrichment Science and Engineering
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Requisition Id 15532 Overview: Oak Ridge National Laboratory (ORNL) seeks a Group Leader to lead groundbreaking research within an area of Material Processing. This position resides in the Materials
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consumption and greenhouse gas emissions from these industries, they are referred to as “energy- and emissions-intensive industries”. Process heating is the dominant use of energy for these applications and
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strategic management and strict adherence to security protocols. We are looking for candidates with extensive experience in either classified HPC data center operations, architecture, parallel computing
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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systems, high-speed parallel file systems, and archival solutions critical to advancing scientific discovery and innovation. As part of ORNL’s leadership-class computing ecosystem, you will play a vital
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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frameworks to maintain secure and compliant environments. Document system architectures, processes, and best practices, and contribute to internal knowledge sharing. Participate in on-call rotations and off
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy