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National Laboratory of the Rockies
Just Landed
2 days ago
Postdoctoral Researcher in Mathematical Optimization for Energy Systems National Laboratory of the Rockies in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full-time annual salary is listed as $76,600–$126,400. Benefits include medical, dental, and vision insurance; short-term disability insurance; pension benefits; a 403(b) employee savings plan with employer match; life and AD&D insurance; paid time off and sick leave; and paid holidays. Eligible positions may include relocation expense reimbursement. Employees may also be eligible for performance-, merit-, and achievement-based awards.
Country
United States
University
National Laboratory of the Rockies
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About this position
The National Laboratory of the Rockies is seeking a full-time Postdoctoral Researcher in Mathematical Optimization for Energy Systems to join the Advanced Computing Solutions Group within the Computational Science Center in Golden, Colorado.
The research focuses on mathematical optimization and its application to the design and control of energy systems. The postdoctoral researcher will help advance automation, control, and decision-making methods for energy applications, working at the intersection of computational mathematics, artificial intelligence, and energy-system engineering.
The project involves formulating and solving large-scale linear, nonlinear, integer, and stochastic optimization problems. A key research direction is the integration of artificial intelligence techniques—particularly reinforcement learning—with classical mathematical optimization formulations and algorithms. The work will also emphasize implementations that effectively use modern parallel computing architectures.
The researcher will collaborate with energy-domain experts to identify suitable optimization applications, develop or adapt mathematical, computing, and simulation frameworks, and evaluate the performance of optimization algorithms and solutions. Responsibilities also include identifying opportunities to enhance classical optimization through AI and reinforcement learning, monitoring relevant research developments, publishing results, and contributing to research proposals.
The role is situated in a multidisciplinary national-laboratory environment with access to advanced computing resources and collaborations spanning industry, academia, and other national laboratories. The position offers opportunities to contribute to energy innovation through computational science, systems analysis, and applied algorithm development.
Funding details
Full-time annual salary is listed as $76,600–$126,400. Benefits include medical, dental, and vision insurance; short-term disability insurance; pension benefits; a 403(b) employee savings plan with employer match; life and AD&D insurance; paid time off and sick leave; and paid holidays. Eligible positions may include relocation expense reimbursement. Employees may also be eligible for performance-, merit-, and achievement-based awards.
What's required
Applicants must be recent PhD graduates within the last three years and meet the educational requirements before the employment start date. Required experience includes formulating optimization problems in an algebraic modeling language such as Pyomo, JuMP, PuLP, or GAMS; experience with mathematical optimization solvers such as CPLEX, Gurobi, Xpress, Cbc, or Ipopt; and a good understanding of computational and mathematical optimization fundamentals. Preferred qualifications include familiarity with MPI or OpenMP, Pyomo and/or JuMP, Python and/or Julia, scalable machine-learning frameworks such as PyTorch, and experience working with diverse, inclusive, and cross-disciplinary research teams. Employment is subject to DOE access requirements, the ability to obtain and maintain a federal PIV card, work authorization, a favorable background investigation, and a pre-employment drug test.
How to apply
Submit an application form for this position through the Workday application portal. Include a cover letter and resume; applications are not retained for future positions.
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