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AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design and implement
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HPC research within past five years. Preferred Qualifications: The ability to work independently and develop and deploy methods at scale. Experience in high-performance computing and software
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will develop materials and processing technologies that will result in a transformational improvement in gallium and Germanium and rare earth magnetic material extraction methods from naturally occurring
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environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Develop strong, productive working relationships across
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/Responsibilities: The successful candidate will be responsible for conducting, coordinating, and reporting complex research assignments related to energy conversion systems including: Design and develop experimental
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-1000 experiment. Develop new low-background materials, scintillators, and production techniques, including custom synthesis of PEN and clean additive manufacturing processes Integrate AI/ML
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designing efficient and trustworthy ML algorithms for physical systems, scalable data loading and training methods for exascale computing platforms, and model verification and validation. The position offers
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level for comparison with carbon/water exchange based on eddy flux measurements Use AI/ML data integration, modeling and trait databases to scale up ecophysiological mechanisms of ecosystem water and
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perpetuation of values and ethics. Develop strong, productive working relationships across partnering research groups within ORNL. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with