Computational Optimization Postdoctoral Researcher

Computational Optimization Postdoctoral Researcher

Entry Level | Full-time
Information Technology/Computing | Livermore, CA | 11/25/2025
Reference #: REF
7719E
Job Code: PDS.
1 Post-Dr Research Staff 1
Organization: Computing
Position Type: Post Doctoral
Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation)
Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana)
Medical Exam: Not applicable Apply Now

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory’s mission.

Pay Range

$138,480 Annually

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.

We have an immediate opening for a Computational Optimizer to conduct research in the areas of stochastic, decentralized, and/or multi-level optimization, with specific application to critical infrastructure systems. You will be an integral part of a multi-disciplinary team of researchers, with skill sets ranging from computer and climate science to power and industrial engineering; projects are typically collaborative with partner academic institutions and other national labs.

You will be developing advanced models of decision-making under uncertainty and adversarial contexts for critical infrastructure operations, planning, and resilience. The ability to conduct fluid engagement with domain experts and end–users to characterize, analyze, and communicate inputs and solutions is critical in this role. Development of advanced mathematical optimization models (e.g., MIP formulations) and scalable (e.g., via decomposition) solvers will be a primary technical focus.

This position is in the Center for Applied Scientific Computing (CASC), which resides within the Computing Directorate  research will be conducted in conjunction with LLNL’s Cyber and Infrastructure Resilience (CIR) program.

In this role you will

• Develop and extend mathematical programming (e.g., MIP, NLP, and MINLP) formulations of core critical infrastructure operations and planning optimization models.
• Design and implement high-performance (parallel) solvers for stochastic, multi-level, and/or decentralized optimization models of critical infrastructure.
• Analyze and mitigate performance bottlenecks in parallel solver implementations.
• Publish research results in external peer-reviewed scientific journals and participate in conferences and workshops.
• Present formal and informal overviews of research progress at group meetings.
• Contribute to grant proposals and collaborate with others in a multidisciplinary team environment to accomplish research goals.
• Pursue independent (but complementary) research interests and interact with a broad spectrum of scientists internal and external to the Laboratory.
• Perform other duties as assigned.

• Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
• Ph.D. in Operations Research, Industrial Engineering, Computer Science, Applied Mathematics, or closely related field.
• Working knowledge of at least one algebraic modeling language (e.g., Pyomo, JuMP, AMPL, and GAMS) for mathematical optimization.
• Working knowledge of at least one widely used mathematical optimization solver (e.g., Gurobi, CPLEX, and Express).
• Experience…