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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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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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scientific datasets. Major Duties/Responsibilities: Designing and developing foundational AI-driven techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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to cutting-edge research aimed at transforming scientific data management and workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent
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for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
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scattering and urban science—to apply and evaluate research methods on large, domain-specific scientific datasets. Major Duties/Responsibilities: Designing and developing foundational statistical/AI/data
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and understand the variation in plant phenotypic traits and response to stresses and link these to genetic information under the Center for Bioenergy Innovation project (https://cbi.ornl.gov/ ). Major
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(https://www.olcf.ornl.gov/frontier ) and plant phenotyping (https://www.ornl.gov/appl ). GPTgp is a pilot project initiated in September 2025 with funding from the US Department of Energy and will
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Director's office can be found here: https://www.ornl.gov/content/research-integrity . Basic Qualifications: A PhD in physics, chemistry, biochemistry or a related field completed within the last five years