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Postdoctoral Researcher: Applied Mathematics, Uncertainty Quantification, and Electrical Grids
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Job Description

The Complex Systems Simulation and Optimization Group has an immediate opening for a full-time, postdoctoral researcher in applied mathematics. Emphasis is placed on computing numerical solutions to partial differential equations, signal processing, and uncertainty quantification (UQ). We are looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NREL.

The successful candidate will collaborate with NREL staff, researchers, and university partners on efforts to develop a rigorous mathematical grounding, numerical methods, signal processing, and modelling for distribution lines for transient event detection in electrical grids. The successful candidate will also contribute knowledge and experience in UQ towards research and development activities paramount to addressing national goals for the utilization of renewable energy and energy efficiency technologies.

Responsibilities include:

  • Assisting NREL and university partner power system researchers to model distribution lines from pole configuration to models that can be simulated in software.
  • Implement and carry out computational experiments in both the simulation and detection of transient events (with the end goal of deploying algorithms on hardware to detect transient events on power grids).
  • Conduct independent and collaborative research in developing and using advanced applied mathematics, UQ, and stochastic optimization for energy systems research including, but not limited to, grid modernization, energy systems integration, and energy analysis.
  • Author, present, and assist in the preparation of technical papers, reports, and conference proceedings on topics related to applied math algorithms and applications to transient event detection, energy systems research, and electric grid modernization.


Required Education, Experience, and Skills

Must be a recent PhD graduate within the last three years.


Preferred Qualifications

  • A strong background in scientific programming, computational applied mathematics (including computing numerical solutions to partial differential equations), and UQ.
  • Experience with programming tools for science and engineering such as Matlab, Julia, and Python (NumPy, SciPy, Matplotlib, Pandas, Scikit-learn), etc.
  • Experience developing parallel programs using both shared memory and distributed memory mechanisms such as MPI, OpenMP, Pthreads, and CUDA is a plus.
  • Experience using stochastic optimization to solve problems in the presence of uncertainty is a plus, as is exposure to optimization modelling languages, e.g. CVX, Pyomo, JuMP.


Submission Guidelines

Please note that in order to be considered an applicant for any position at NREL you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.


EEO Policy

NREL is dedicated to the principles of equal employment opportunity. NREL promotes a work environment that does not discriminate against workers or job applicants and prohibits unlawful discrimination on the basis of race, color, religion, sex, national origin, disability, age, marital status, ancestry, actual or perceived sexual orientation, or veteran status, including special disabled veterans.

NREL validates right to work using E-Verify. NREL will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new employee’s Form I-9 to confirm work authorization. For additional information, click here.

About National Renewable Energy Laboratory

At NREL, we focus on creative answers to today's energy challenges. From breakthroughs in fundamental science to new clean technologies to integrated energy systems that power our lives, NREL researchers are transforming the way the nation and the world use energy.