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AI/ML surrogate models for inverse design of new materials and processes, incorporating simulated and experimental multi-modal datasets. Develop AI/ML approaches to bridge length- and time-scales in
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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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science, and materials engineering, with emphasis on understanding material behavior in complex chemical and radiological environments. Research activities may include the design of functional nanomaterials
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the behavior of complex rotating machinery operating under extreme conditions. Through the integration of modeling, simulation, and experimental validation, the group supports the design, performance
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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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include, but not limited to, one or more of the following: Design and implement system software to enable AI-readiness for scientific data by developing adaptive techniques capable of maintaining
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systems (Mojo, Julia, Rust, Python), and HPC system co‑design. This position is embedded within the larger DOE ASCR ecosystem, with direct relevance to ongoing efforts, and related AI‑for‑HPC thrusts
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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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polymerization of polymers coupled with autonomous chemistry https://www.ornl.gov/project/precision-synthesis-polymers-mastering-reaction-equilibria . This position resides in the Soft Materials and Membranes
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supports the design, performance optimization, and reliability of gas centrifuges used in the processing of uranium and stable isotope compounds. Within this organization, the Distinguished Rotor Dynamics