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carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory-informed AI-models for creating
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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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Requisition Id 15797 Overview: We are seeking a postdoctoral research associate who will study the dynamics of high-intensity proton beams in the Spallation Neutron Source (SNS) ring. This project
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relationships via digital manufacturing practices Basic experience with AI/ML techniques Publish research in peer reviewed journals and conferences Support R&D staff members on their projects General support of
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divisions at ORNL. Major Duties/Responsibilities: Conduct materials research using aberration-corrected STEM and EELS. Collaborate with project team members within and outside ORNL. Present and report
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evaluate applications based on candidate excellence and relevance of the proposed research topic. ● Proposed Seaborg Researcher research project ○ Creativity and innovation ○ Impact on program’s mission
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artifacts (journal articles). Almost all of our simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor
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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