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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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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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research methods on large, domain-specific scientific datasets. Major Duties/Responsibilities: Designing and developing foundational AI-driven techniques for the generation and exploration of complex, large
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domain experts—such as those in neutron scattering and urban science—to apply and evaluate research methods on large, domain-specific scientific datasets. Major Duties/Responsibilities: Designing and
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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
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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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(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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and new/advanced target designs. These methodologies principally include thermal diffusivity, thermal conductivity, and thermal expansion but also could include other mechanical and chemical properties