Postdoctoral Research Associate - Sparse Algorithms

UT-Battelle; Oak Ridge (TN); On-site; USD 65,000 - 75,000; Full time

Job summary

Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher to develop state-of-the-art sparse algorithms for large-scale numerical, scientific, and AI models. The position uses Frontier, the world’s first exascale system, and involves collaboration with experts in ML, optimization, and imaging.

The role emphasizes publications, presentations, and open-source software in addition to mentoring students and contributing to DOE mission applications.

Qualifications

• PhD completed in a relevant field with strong publication record or demonstrated software/research experience.
• Evidence of depth in at least one specialization through papers, code, or comparable work.

Responsibilities

• Design sparse algorithms for large-scale numerical, scientific, and AI models.
• Demonstrate scalability on leadership supercomputers for national-scale problems.
• Develop mathematical analyses to bound trade-offs between performance and energy efficiency.
• Communicate findings via publications and talks at top venues.

Skills

Publications

Software

Research

Education

PhD in Computer Science, Applied Mathematics, Computational Science, or related discipline

Job description

Overview

The Discrete Algorithms Group at Oak Ridge National Laboratory (ORNL) seeks a postdoctoral researcher for a two-year position specializing in sparse algorithms. This researcher will focus on advancing secure, trustworthy, and efficient AI solutions for scientific applications by developing state-of-the-art sparse algorithms in matrices, tensors, and networks. These algorithms will support large-scale numerical, scientific, and AI models. The researcher will also disseminate findings through publications and presentations in top-tier peer-reviewed journals and conferences. To conduct this work, the successful candidate will use the world's first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric grid analytics, and scientific imaging.

Major Duties/Responsibilities Include, But Are Not Limited To

• Designing novel sparse algorithms - including mixed-precision, energy-efficient, and accelerator-optimized methods - for large-scale numerical, scientific, and/or AI models.
• Demonstrating the scalability of new algorithms on leadership supercomputers for large-scale problems of national and societal interest.
• Developing mathematical analyses to bound the trade-offs between performance, energy efficiency, and time, especially in the context of sparse computations.
• Communicating research via publications and conference presentations in top venues, invited talks, and organized workshops, tutorials, and symposiums.
• Releasing research software as open-source libraries and contributing to community codes.
• Collaborating with domain scientists to apply sparse algorithms to DOE mission applications and mentoring and working with undergraduate and graduate students.

Basic Qualifications

• A PhD in Computer Science, Applied Mathematics, Computational Science, or a related discipline.
• Demonstrated depth in at least one of the areas of specialization listed below, evidenced by publications, software, or comparable research