NLR (formerly NREL): Postdoc: Mathematical Optimization for Energy Systems

This is a fixed-term, full-time postdoc at the National Laboratory of the Rockies (NLR). It is in the Advanced Computing Solutions Group of the Computational Science Center. The work applies mathematical optimization to the design and control of energy systems. It may also combine AI methods such as reinforcement learning with classical optimization. It suits a recent PhD with strong optimization experience.

What you'll do

• Work with domain experts to find where mathematical optimization is a good approach and keep up with optimization research
• Adopt or develop the mathematical, computing and simulation frameworks needed to implement and evaluate optimization algorithms
• Look for ways to use AI and reinforcement learning to improve classical optimization algorithms or formulations
• Use parallel algorithms for large-scale linear, nonlinear, integer and stochastic optimization problems
• Write publications and contribute to proposals that sustain research directions

Who can apply

• A PhD completed within the last three years, with the educational requirements met before the start date
• Experience formulating optimization problems in an algebraic modelling language such as Pyomo, JuMP, PuLP or GAMS
• Experience with optimization solvers such as CPLEX, Gurobi, Xpress, Cbc or Ipopt
• A good grasp of the computational and mathematical fundamentals of optimization
• Preferred: MPI and OpenMP, Python or Julia, and scalable machine learning frameworks such as PyTorch
• Must be able to meet U.S. Department of Energy site access rules and obtain a federal PIV card, which requires a background check

How to apply

Apply on NLR's Workday page and include a cover letter and a resume. The page says the closing window is up to 30 days and may be extended, and it gives no fixed date.

Official page: nrel.wd5.myworkdayjobs.com/en-US/NLR/job/Postdoctoral-Researcher---Mathematical-Optimization-for-Energy-Systems_R14352