Computational Optimization Postdoctoral Researcher jobs in United States
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Lawrence Livermore National Laboratory · 1 month ago

Computational Optimization Postdoctoral Researcher

Lawrence Livermore National Laboratory (LLNL) is seeking a Computational Optimization Postdoctoral Researcher to conduct research in stochastic, decentralized, and multi-level optimization for critical infrastructure systems. The role involves developing advanced models for decision-making under uncertainty and collaborating with a multidisciplinary team to enhance infrastructure resilience.

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Responsibilities

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

Qualification

Mathematical optimizationAlgebraic modeling languageHigh-level programmingAdvanced optimization solversHigh-performance computingGeospatial information analysisAnalytical skillsProblem-solving skillsCommunication skillsTeam collaboration

Required

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 developing software in a high-level language such as Python, Julia, and C++ (Python preferred)
Experience developing advanced optimization solvers considering either adversarial (multi-level) behaviors, uncertain inputs, or decentralized contexts
Publication record in high-quality peer-reviewed journals and/or conferences
Analytical and problem-solving skills necessary to craft creative solutions to independently solve complex problems
Proficient verbal and written communication skills to effectively collaborate in a team environment, present and explain technical information to technical as well as non-technical audiences, document work and write research papers

Preferred

Experience with high-performance computing systems, specifically parallel programming libraries such as MPI
Experience with the application of mathematical optimization to critical infrastructure systems, including electricity grid and natural gas networks
Experience processing and analyzing geospatial information, including climate and weather data

Benefits

Flexible Benefits Package
401(k)
Relocation Assistance
Education Reimbursement Program
Flexible schedules (*depending on project needs)

Company

Lawrence Livermore National Laboratory

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Lawrence Livermore National Laboratory, a national security laboratory, provides transformational solutions to national security challenges.

Funding

Current Stage
Late Stage
Total Funding
$11.4M
Key Investors
ARPA-EUS Department of EnergyDARPA
2023-11-21Grant
2023-08-14Grant
2022-09-19Grant

Leadership Team

G
Greg Herweg
Chief Technology Officer
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D
David Shaughnessy
Deputy Chief Financial Officer
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