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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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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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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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, including: Surrogate models or learned potentials Generative models for biomolecular design Representation learning for biomolecular systems Familiarity with protein–protein interaction (PPI) networks
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to accelerate the design and discovery of novel materials. The Materials Theory Group has a background in using first principles methods to examine electronic and thermal transport, magnetic properties
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-layer (i.e., large aspect ratio) meshing capabilities. Additional application methods of interest include adaptive meshing for design/shape optimization as well as solution optimization. In
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-based systems Background in topology optimization and structural design Experience in thermomechanical characterization of polymer materials Demonstrated experimental capabilities and a strong
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, Research Accelerator Division, Neutron Sciences Directorate at Oak Ridge National Laboratory (ORNL). The successful candidate will work closely with SNS research and operations staff to design and carry
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. Scalability of Preprocessing Pipelines: Design and implement automated, parallel preprocessing workflows capable of handling multi-petabyte datasets efficiently while reducing throughput bottlenecks. Data