3-5 years of experience
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Employment Type:
Full time
Job Category:
Postdoctoral Researcher: Artificial Intelligence for Energy Systems
(This job is no longer available)
Grad Date

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Job Description

The Complex Systems Simulation and Optimization Group in the NREL Computational Science Center has an opening for a full-time postdoctoral researcher in artificial intelligence (AI) for energy systems, with special emphasis on deep reinforcement learning for AI control. Control/sequential decision problems arise in a variety of areas of renewable energy research including power system planning and management, wind farm optimization, battery management, building energy management, transportation, etc. The successful candidate will collaborate to develop, adapt, improve, and scale cutting edge AI methods to real world projects in support of NREL and EERE mission and goals. 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 and researchers, other national labs, and universities on efforts to develop and apply AI at scale to real-world problems in renewable energy research, with specific emphasis on modernization of the nation’s grid, building, and transportation infrastructure and operation to support the 100% renewable energy scenario. In particular, the candidate will participate in a large interdisciplinary effort to establish the foundations of so-called “autonomous energy systems”, in which quasi-independent agents interact to seamlessly integrate and control large numbers of distributed energy resources.


Required Education, Experience, and Skills

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


Preferred Qualifications

Preferred experience includes:

Expertise in Markov Decision Processes, deep learning (neural networks, including knowledge of core mathematical underpinnings), reinforcement learning (esp., for continuous and/or large state-action spaces).

Expertise in related AI concepts, including, but not limited to: Monte Carlo Tree Search, policy gradients, actor-critic methods, model-based acceleration, generative adversarial networks, trust region policy optimization, guided policy search.

Experience with classical control theory, model predictive control, dynamic programming.

Experience with game theory, complex systems theory, agent-based learning.

Strong background in mathematics, statistics, and probability.

Strong background in physics and engineering, esp. of power systems, grid-integrated buildings, and/or transportation systems.

Strong programming skills, especially experience using deep learning frameworks (e.g. TensorFlow) on high performance computing/GPU platforms.


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.