Requisition Id 16485

Overview:

The Data and AI Systems Research Section within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance computing (HPC), with an emphasis on developing intelligent systems that can accelerate large-scale scientific research on leadership-class supercomputers.

The successful candidate will contribute to research efforts supported by the U.S. Department of Energy Office of Science, including the Advanced Scientific Computing Research (ASCR) program and the Genesis initiative. These programs focus on integrating AI directly into scientific workflows to enable autonomous, data-driven discovery in areas such as fusion energy, materials science, climate science, and nuclear energy.

As part of ORNL's interdisciplinary research environment, you will work alongside scientists, engineers, and computational researchers while leveraging world-class computing resources, including Frontier, the world's first exascale supercomputer. The role includes developing and advancing open-source software for large-scale hyperparameter optimization (HPO), neural architecture search (NAS), and Bayesian optimization on distributed HPC systems.

Research activities will address key challenges in AI for science, including surrogate modeling, uncertainty quantification, and multi-fidelity optimization for complex simulation workflows. This position offers an opportunity to contribute to cutting-edge AI and HPC research while supporting DOE's broader mission to advance scientific innovation through computational science.

The appointment length is 2 years with the possibility of extension, subject to performance and availability of funding.

Major Duties and Responsibilities:

• Conduct research and development in scalable AI/ML methods for scientific computing and high-performance computing environments.
• Develop and evaluate optimization techniques for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies.
• Contribute to research in uncertainty quantification, surrogate modeling, and other methods that improve the robustness and reliability of AI-driven scientific applications.
• Design and implement distributed and parallel approaches that efficiently leverage large-scale computing resources, including heterogeneous CPU/GPU systems, along with the possibility of working with Quantum computing.
• Collaborate with interdisciplinary research teams to integrate AI/ML capabilities into scientific simulation, data analysis, and computational workflows.
• Contribute to the development and maintenance of open-source software, including testing, documentation, and user support activities.
• Work closely with researchers and domain scientists to communicate results, define research directions, and support collaborative projects.
• Publish research findings in peer-reviewed journals and present work at scientific workshops and conferences.
• Design and implement scalable AI/ML optimization algorithms for hyperparameter optimization and neural architecture search, targeting scientific machine learning models running on leadership-class HPC systems.
• Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace - in how we treat one another, work together, and measure success.

Basic Qualifications:

• A PhD in Computer Science, Applied Mathematics, Computational Science, Data Science, or a related discipline completed within the last three years.
• An excellent record of productive and creative research as demonstrated by publications in top peer-reviewed journals and conferences.
• Demonstrated experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and hyperparameter optimization or AutoML techniques.
• Proficiency in Python and familiarity with software engineering best practices (version control, testing, documentation).
• Experience with HPC environments and parallel/distributed computing.
• Strong problem-solving and communication skills, with the ability to work collaboratively in a team setting.

Preferred Qualifications:

• Experience with multi-fidelity optimization, neural architecture search, or large-scale AutoML systems.
• Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific applications.
• Prior exposure to DOE workflows, national laboratory environments, or large-scale simulation codes.
• Experience contributing to open-source scientific software projects.

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This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.