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at the forefront of science and technology; informs R&D to advance the field of knowledge and/or technology in one’s respective specialty; sets, implements, and models standards for performance of work consistent
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technology; collaborates with members of other data-intensive groups at ORNL to provide cross-discipline approaches; sets, implements, and models standards for performance of work consistent with Environment
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, drawings, bills of materials, specifications, inspection plans, assembly instructions, and test plans/procedures. Utilize SolidWorks 3D CAD to create models and drawings in accordance with ASME Y14.5
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Plant Phenotyping Laboratory (https://www.ornl.gov/appl ). Perform phenotypic characterizations of transgenic and genome-edited lines in poplar and other model or bioenergy plants Perform molecular and
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. Traditional and object-oriented programming. Model View Controller concept. SAP ABAP (classic and object-oriented), SAP Business Server Pages (BSPs), SAP WebDynpro and SAP WebRFC technologies. SAP Cloud
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expertise; works to solve technical challenges for science, engineering, or technical projects and functions; sets, implements, and models standards for performance of work consistent with Environment, Safety
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Qualifications: Ideal candidates will possess a background in nuclear engineering. Previous experience using thermal hydraulics models and codes Previous experience using the MOOSE framework. Familiarity with
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) technologies. Experience with AI/ML concepts and tools (e.g., scikit-learn, TensorFlow, or similar) for basic data analysis and predictive modeling. Experience with relational databases (e.g., PostgreSQL, MySQL
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and experience may be considered). Knowledge and experience with SAP SuccessFactors and related integrations. Traditional and object-oriented programming. Model View Controller concept. SAP ABAP
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Substantial programming skills using Python or modern C/C++ Experience with machine learning and deep learning libraries Experience building AI models in platforms such as TensorFlow, Keras, or PyTorch