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Laboratory (ORNL) invites outstanding candidates to apply for a staff position in the Data Analysis and Machine Learning Group. This group focuses on scientific computing with a strong emphasis on scientific
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pay range: Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay. Salary for this position will be commensurate with
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equipment conditions. Maintain records of facility operations (e.g. narrative logs, rounds, test data, etc.). Participate in research and development activities associated with isotope enrichment. SIPF will
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of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications: Ph.D. in mathematics, engineering, data/computational science
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scientific computing, control theory, data science, data driven methods, discrete mathematics, graph algorithms, high-performance computing, integral equations and nonlocal models, linear and multilinear
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other nuclear facilities, and an assemblage of world-leading scientists and engineers. Please visit https://www.ornl.gov/directorate/isotopes for more information about ISED. Benefits at ORNL: ORNL
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to enable knowledge discovery from scientific data through efficient data management, reduction, and analysis. The group's goal is to accelerate scientific knowledge by researching and developing novel
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instrumentation, will ensure U.S. leadership in neutron scattering for decades to come. Please see http://neutrons.ornl.gov/sts for more information. Major Duties/Responsibilities: Take complex engineering designs
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consisting of the best-of-the-best science teams around the world. NCCS enables these teams to perform science that is just not possible anywhere else. In return, and instead of charging for compute and data
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consisting of the best-of-the-best science teams around the world. NCCS enables these teams to perform science that is just not possible anywhere else. In return, and instead of charging for compute and data