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-class research and development in hydrologic and atmospheric system modeling and evaluation, large scale data analytics and ML, and model-data integration at the US Department of Energy’s (DOE’s
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field involving geospatial computing or big data architectures. Experience with one or more of following languages: Python, C/C++, R, C#, Java; knowledgeable in Linux, shell scripting. Experience with
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. This position supports technical exchanges with foreign counterparts as necessary, and is responsible for integrating the collected raw information with an existing classified electronic document management
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the Geospatial Science and Human Security Division (GSHSD) at ORNL. The group performs artificial intelligence, computer vision, and federated learning research initiatives, with emphasis on large scale geospatial
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Power BI development and a proven foundation in Data Science, including fundamental-level skills in Generative AI and Machine Learning! This role focuses on designing, developing, and moving BI, Analytics
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Power BI development and a proven foundation in Data Science, including fundamental-level skills in Generative AI and Machine Learning! This role focuses on designing, developing, and moving BI, Analytics
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, Energy Science and Technology Directorate at Oak Ridge National Laboratory (ORNL). If selected, you will contribute to the understanding of processing structure property relationships in large scale
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/Responsibilities: Participate in new and ongoing research in quantum communications, quantum networking, and quantum repeaters. Formulate novel quantum information solutions to problems of national interest and
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computer language and version control software. Demonstrable knowledge of transportation and logistics. Previous experience with large datasets, including data mining and analysis. Excellent communications
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. Familiarity with Azure Open AI Experience in several of the following areas: database architecture, ETL, business intelligence, big data, machine learning, advanced analytics. Understanding of authentication