Post Doctorate Research Associate - Mathematics

Overview

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.

Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.

The Physical and Computational Sciences Directorate's (PCSD’s) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace  our most important resource is our people—experts across the range of scientific disciplines who team together to take on the biggest scientific challenges of our time.

The Pacific Northwest National Laboratory Computational Mathematics group is seeking candidates for a Post Doctorate Research Associate (Mathematics). The Computational Mathematics Group has strong capabilities in combinatorial algorithms, optimization, and data-driven methods.

• Develop new approaches for analyzing massive graphs, hypergraphs, and related combinatorial structures.
• Apply probabilistic methods to design and analyze randomized graph algorithms.
• Design graph approaches for scientific machine learning.
• Develop and optimize high-performance numerical algorithms for large-scale scientific computing.
• Interact, communicate, and solve problems with a diverse team of applied mathematics, computational science, and experimental research staff within the group and across PNNL.
• Publish your results in high-quality, peer-reviewed journals.
• Present your research at technical conferences and project and program review meetings.
• Participate in the development of research proposals.

Minimum Qualifications:

• Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.

Preferred Qualifications:

• Ph.D. in Mathematics, Computer Science, or a related field.
• Background in discrete mathematics, graph theory, or network science.
• Foundational background in probability and linear algebra, including the design of randomized algorithms.
• Experience applying advanced graph-theoretic methods to practical domain problems.
• Research or experience with Graph Neural Networks.
• Proven track record in preparing manuscript submissions to scientific journals.
• Proficiency in developing scientific software.

Not Applicable.

This is not a Testing Designated Position (TDP).

Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!

At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.

Our laboratory is committed to fostering a work environment where all individuals are treated with fairness and respect while solving critical challenges in fundamental sciences, national security, and energy resiliency. We are an Equal Employment Opportunity employer.

Pacific Northwest National Laboratory (PNNL) is an Equal Opportunity Employer. PNNL considers…