290 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "U.S" "St" positions at Oak Ridge National Laboratory
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on-premises and in the cloud. Work with researchers and developers to configure complex pipelines to streamline multiple code repositories for automated deployments/upgrades, and work with others across
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coding (Python) for building energy modeling and controls Preferred Qualifications: Expertise in modern optimal control techniques (e.g., AI based controls) High level of competence in coding and scripting
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in Computer Science, Data Science, Applied Mathematics, or a related field. Strong Python development skills and familiarity with git, CLI tooling, VS Code Proficiency with PyTorch and/or TensorFlow
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those skills to a variety of problems, and the ability to determine and understand the broader context of his or her research. Preferred Qualifications: Proficiency in multiple modern coding languages is
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, regardless of state laws. For foreign national candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a
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and Technology Division, Physical Sciences Directorate at Oak Ridge National Laboratory (ORNL). Working in support of the U.S. Department of Energy’s (DOE) Office of Fusion Energy Sciences (FES), Office
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initiatives to support Laboratory operations. The QR will support the development and implementation of quality requirements supporting the ORNL Isotope Program including IS0-9001 and the U.S Department
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that integrate with existing or new large language models and large vision models for resource optimization with energy grid data. Provide coding support to implement privacy preserving federated learning
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quantum critical spin systems). Experience with writing high-quality code. Excellent record of productive and creative research demonstrated by publications in peer-reviewed journals. Excellent written and
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
familiarity with AI/ML algorithms, for generative materials design, or for knowledge extraction, e.g. causal ML or symbolic regression, etc. Strong demonstrated background in coding for data analysis using