Oak Ridge National Laboratory · 6 hours ago
Postdoctoral Research Associate, Data Readiness
Oak Ridge, TN
Full-time
Onsite
New Grad, Entry LevelOak Ridge National Laboratory is seeking a postdoctoral research associate to advance the state of scientific AI by addressing cross-cutting challenges in data readiness for AI. The role involves researching, designing, and deploying innovative data pipelines and readiness frameworks to enhance AI-driven discovery in various scientific domains.
Advanced MaterialsClean EnergyEnergyEnergy ManagementManufacturingNuclearRenewable Energy
Responsibilities
Conduct and publish original research focused on data readiness methodologies and frameworks for scalable AI applications across fluid dynamics, fusion, materials, life sciences, and other strategic domains
Investigate novel approaches for balancing efficient I/O, interoperability, and scientific validity in AI-ready datasets
Design, prototype, and optimize preprocessing pipelines using HPC resources, targeting scalable execution and automation
Collaborate with domain scientists to integrate pipelines into end-to-end AI workflows specific to scientific domains
Publish research outcomes in peer-reviewed journals and conference venues, setting benchmarks and proposing methodologies for cross-disciplinary readiness challenges
Aid in the development and adoption of open standards for scientific dataset processing, including contributing to open-source tools
Mentor interns, students, and peers in cross-domain data readiness approaches
Present findings at technical workshops, scientific meetings, and in outreach efforts to improve awareness around the importance of data readiness for scientific AI
Qualification
Required
Ph.D. earned in Computer Science, Data Science, Computational Science, a scientific domain relevant to AI (e.g., physics, biology, chemistry, climate), or a closely related field (within the last 5 years or near completion)
Demonstrated expertise in data preprocessing pipelines, AI-ready dataset design, or scientific workflows in HPC environments
Proven experience with modern data frameworks (e.g., PyTorch, TensorFlow), scalable I/O solutions (e.g., HDF5, ADIOS2), and distributed computing tools relevant to data preparation
Evidence of ability to conduct independent research and publish in peer-reviewed venues
Preferred
Hands-on experience prototyping and scaling data pipelines in HPC environments (Frontier-scale or similar)
Strong familiarity with domain-specific formats such as NetCDF, CSV/Parquet, FASTA/MMCIF, or graph-based encodings in materials and molecular AI
Familiarity with frameworks for automated and reproducible workflows
Knowledge of governing regulations around privacy (e.g., HIPAA, ITAR), including secure enclave architectures and federated learning approaches
Background in developing reproducible pipelines with validation, provenance tracking, and schema consistency checks
Publications in relevant conferences (e.g., NeurIPS, SC, AAAI, or domain-specific venues like Fusion Science or Computational Materials)
Collaborative mindset in team environments and across disciplines
Company
Oak Ridge National Laboratory
Oak Ridge National Laboratory holds a range of R&D assignments, from fundamental nuclear physics to applied R&D on advanced energy systems.
Founded in 1943
5001-10000 employees
H1B Sponsorship
Oak Ridge National Laboratory has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
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Trends of Total Sponsorships
2025 (268)
2024 (276)
2023 (223)
2022 (228)
2021 (192)
2020 (152)
Funding
Current Stage
Late StageTotal Funding
$9.8MKey Investors
US Department of Energy
2023-09-21Grant· $4.8M
2023-07-27Grant
2022-03-14Grant· $5M
Leadership Team
Arjun Shankar
Division Director, National Center for Computational Sciences, Oak Ridge National Laboratory
Brett Ellis
Division Director - Research Computing Support
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