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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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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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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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-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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, this position is a Workplace Substance Abuse (WSAP) testing designated position. WSAP positions require passing a pre-placement drug test and participation in an ongoing random drug testing program. Visa
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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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available for added convenience. Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits
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). Major Duties/Responsibilities: Designing, developing, and conducting experiments related to data center thermal management technologies, phase change heat transfer processes, dehumidification systems