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Postdoctoral Researcher – Mathematical Optimization for Energy Systems.

🚀 Off-cycle Internship


🤑 $73.2K - $120.8K

AI generated summary

  • You must have a recent PhD, experience in formulation and solving optimization problems, understanding of optimization fundamentals, familiarity with distributed computing and programming languages, and experience working in diverse research teams.
  • You will collaborate with experts to apply mathematical optimization for energy systems, develop frameworks, explore AI/RL enhancements, and publish research.

Off-cycle Internship

Research & Development, DataGolden


  • The Complex Systems Simulation and Optimization (CSSO) Group in the NREL Computational Science Center has an opening for a full-time Postdoctoral Researcher – Computational Science, with emphasis on mathematical optimization and its application to the design and control of energy systems. We are looking for a dynamic researcher with a strong technical background to help us transform our renewable energy future through advanced automation, control and decision making.
  • The successful candidate will have extensive experience with mathematical optimization formulations and algorithms and their application to physical systems. Additionally, the candidate will be familiar with parallel algorithmic approaches for large-scale linear, nonlinear, integer, and stochastic optimization problems. We anticipate that the research will involve integrating Artificial Intelligence (AI) techniques, such as reinforcement learning (RL), with classical mathematical optimization approaches and implementations. We seek candidates capable of pursuing research directions that combine these algorithmic components, using implementations that are suitable for effective utilization of the modern parallel computing architectures that are available at NREL. Candidates with creative problem-solving skills, interest in cross-disciplinary collaboration, and a passion for the mission and goals of both NREL and EERE are of particular interest.


  • Basic Qualifications:
  • Must be a recent PhD graduate within the last three years.* Must meet educational requirements prior to employment start date.
  • Additional Required Qualifications:
  • Experience formulating optimization problems in an algebraic modeling language, e.g., Pyomo, JuMP, PuLP, GAMS.
  • Experience with mathematical optimization solvers, e.g., CPLEX, Gurobi, Xpress, Cbc, Ipopt, and their capabilities.
  • Good understanding of optimization fundamentals, both computational and mathematical.
  • Preferred Qualifications:
  • Familiarity with distributed computing frameworks such as MPI and OpenMP
  • Experience with Pyomo and/or JuMP
  • Experience programming in Python and/or Julia
  • Experience with scalable machine learning frameworks, e.g, PyTorch
  • Experience working with diverse, inclusive, and cross-disciplinary research teams

Education requirements

Currently Studying

Area of Responsibilities

Research & Development


  • Collaborate with domain experts to identify where mathematical optimization constitutes a viable approach and maintain awareness of optimization-related research both at NREL and in the literature more generally.
  • Adopt existing – or develop new – mathematical, computing, and simulation frameworks required to implement and evaluate the performance of optimization algorithms and solutions.
  • Creatively identify new opportunities to leverage AI/RL to augment or enhance classical optimization algorithms and/or formulations.
  • Author publications and contribute to proposals to sustain research directions.


Work type

Full time

Work mode





73200 - 120800 USD