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. The Oak Ridge National Laboratory (ORNL) is one of the nation’s largest multi-program science and technology laboratories within the U.S. Department of Energy (DOE). The Enrichment Science and Engineering
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Qualifications: Ph.D. (within 0–5 years) in computational bioscience, computational biophysics, computer science, or a related field Strong programming skills in C++, Python, or similar scientific computing
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drug testing program. In addition, due the SCI, you may also be subject to random polygraph testing. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has
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challenging and impactful research and development programs in healthcare informatics, bioinformatics, high performance computing and deep learning. We have a collaborative environment focusing on designing
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of emerging architectures and performance for advanced computing. This position resides in the Architectures and Performance Group in the Advanced Computer Systems Research (ACSR) / Computer Science Mathematics
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, or a related discipline The ability to obtain and maintain a security clearance from the Department of Energy Preferred Qualifications: Experience with programming languages such as python, Fortran
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comprehensive nuclear security program enhancements. Assist DTRA in establishing and advancing performance testing programs through the provision of testing equipment and guard force training to support Tabletop
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test and participation in an ongoing random drug testing program. Visa Sponsorship: Visa sponsorship is not available for this position. About ORNL: As a U.S. Department of Energy (DOE) Office of Science
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. Formulating necessary solutions using various parallel computing paradigms and tools, HPC schedulers (such as slurm), Containers and Kubernetes, Python, Bash and other scripting/programming languages in
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, Environmental Informatics, or a closely related field. 3+ years of strong programming experience in Python (including data processing, scripting, or basic ML workflows). Foundational understanding of ML concepts