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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal
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awareness and understanding of open relevant research conducted throughout the DOE complex and the world. Basic Requirements: PhD in Mechanical Engineering, Electrical or Computer Engineering, or related
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making
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or PhD in Computer Science, Computer Engineering, Cybersecurity. Experience architecting and implementing complex distributed systems and willingness to learn Experience in cluster computing and scaling
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or PhD in Computer Science, Computer Engineering, Cybersecurity, or related fields with 2 years of experience. Experience architecting and implementing complex distributed systems and willingness to learn
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researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance
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and continuous learning. Stakeholder Engagement & Partnerships: Serve as the external interface for the center: liaise with ORNL counterparts addressing lab-wide computing and data initiatives, build
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, machine learning, artificial intelligence, and predictive analytics capabilities for advanced manufacturing systems and composite structures. Design and execute experimental validation activities to verify
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid