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National Laboratory (ORNL). This is an opportunity to develop novel tools and techniques across scales (river basin to national), publish in top-tier journals, collaborate with industry and academic partners
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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MSTD on the physical metallurgy of aluminum alloys for various applications including automotive, aerospace, and thermal management. Projects will include application of physical metallurgy principles
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industrial heat pumps). The role also involves research and analysis of technologies and practices for increasing the energy efficiency of the industrial sectors. The primary objective is to deliver the direct
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management skills are required. This position resides in the Deposition Science and Technology Group at the Manufacturing Demonstration Facility (MDF) in the Manufacturing Sciences Division (MSD), Energy
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supercomputing resources. The candidate will also contribute to the development of large-scale identity and key management solutions. We are a leader in computational and computer science, with signature strengths
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applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and
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management, workflow systems, analysis and visualization technologies, programming systems and environments, and system science and engineering. Major Duties/Responsibilities: The position requires
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both independently and within interdisciplinary, collaborative research teams, including mentoring students, managing multiple priorities in a fast-paced environment, and adapting to evolving research
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from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and