243 data "https:" "https:" "https:" "https:" "Dr" "L2CM" positions at Oak Ridge National Laboratory
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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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. This section will advance the integration of high‑performance computing (HPC), artificial intelligence (AI), data science, and automation with experimental biosciences to enable predictive, scalable, and AI
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accordance with ORNL and DOE requirements. Complete HSPD-12/PIV (UPIV) credential enrollment, activation, and issuance processes. Capture biometric data (photographs, fingerprints) as required
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organizations. Champion the development of actionable recommendations from a set of possibilities and synthesize findings well. Coordinate and support the annual report data calls in communicating the performance
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into compelling multimedia and executive-ready formats. The Computing and Computational Sciences Directorate advances high-performance computing, artificial intelligence, data science, quantum computing, and
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development teams Basic Qualifications: A Bachelor's degree in Computer Science, Electrical or Computer Engineering, or other field relevant to the job duties. Competency in Python and C++ programming Ability
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data connectivity. This role ensures that all changes to control configurations, communications pathways, or system integrations are made in accordance with Industrial Control System / Operational
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and clustered computing services to researchers who process large data sets and/or develop code as a part of their project. Ensure the availability, performance, scalability, and security of production
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Requisition Id 15882 Overview: We are seeking a Research Staff Scientist to advance our fundamental understanding in 2D materials, thin films, and hybrid nanostructures for quantum information
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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