74 software-formal-method-phd Postdoctoral positions at Oak Ridge National Laboratory in United States
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
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interconnection topologies that simplify the use of diverse distributed storage resources through advanced methods for distributed data placement, layout, tiering, and movement. Major Duties and Responsibilities
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, and measure success. Basic Qualifications: PhD in Plasma Physics, Electrical Engineering, Nuclear Engineering, or a closely related field completed within the last 5 years, or expect to complete a PhD
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. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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, optical spectroscopy, and scanning probe microscopy. Advance capabilities for specialized scanning probe microscopy methods Work as part of a dynamic team conducting research that advances and develops
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Science group addresses this mission through the development of quantum computational methods and software for diverse scientific applications. Major Duties/Responsibilities: Conduct research in
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Requisition Id 16562 Overview: The Analytics and AI Methods at Scale (AAIMS) group at the National Center of Computational Science (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking
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and lignocellulosic biomass into domestic fuels and chemicals. Research activities may include creating transformation methods, constructing gene‑expression “genetic parts,” and developing CRISPR‑Cas
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databases to assess ecosystem response to environmental forcing. The primary research focus is AI-forward, experiment-driven. This position encourages leveraging AI/ML methods to assess plant physiological