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to contribute to development of alloys with desirable advances in mechanical properties, thermal/electrical properties, and processability. A background in solidification processing, high pressure die casting
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating
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, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in Hydrology, Water
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/device models into open-source software tools for integrated system dynamic and transient simulations. Integrate post-processing measures for simulations to help with automation. Deliver ORNL’s mission by
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, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in Computer
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD degree in Computer Science or a related discipline. A strong background in scientific data
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environment consisting of mathematicians, computational and computer scientists, and domain scientists conducting basic and applied research in support of ORNL’s mission. Specific responsibilities include
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team