46 phd-data-mining Postdoctoral positions at Oak Ridge National Laboratory in United-State
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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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postdoctoral research associate to advance the state of scientific AI by addressing cross-cutting challenges in data readiness for AI to enable scalable, reproducible AI workflows on leadership-class systems
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interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Basic Qualifications: A PhD in in condensed matter physics, theoretical physics, quantum information, or a closely related
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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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strengths in high-performance computing, system architecture, and data analytics with applications in a large variety of science domains. NCCS is home to some of the fastest supercomputers and storage systems
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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of computational scientists, computer scientists, experimentalists, materials scientists, and conduct basic and applied research in support of the Laboratory’s mission. Engage with the broader community
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in high-performance computing and data analytics with applications in a large variety of science domains and NCCS is home to some of the fastest supercomputers and storage systems in the world
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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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