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simulation and flood inundation modeling. River basin planning and operations modeling, including reservoir simulation and optimization. Hydrodynamic modeling of water temperature and quality constituents
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), including data analysis and some modeling. Work with others to maintain a high level of scientific productivity; the job holder will interact regularly with senior scientists, program managers, and group
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at ORNL, along with computational tools for integrated atomistic modeling in support of materials research for extreme environments. The candidates will develop and apply advanced experimental
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of scientific AI. Focus Areas: Cross-Domain Interoperability: Develop common readiness templates, standardized metadata models, and APIs to enable seamless integration across diverse scientific domains
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and