National Renewable Energy Laboratory Researcher - Applied Mathematics and Machine Learning For Clean Energy in Golden, Colorado
Researcher - Applied Mathematics and Machine Learning For Clean Energy
CO - Golden
Hours Per Week
The Complex Systems Simulation and Optimization (CSSO) group within NREL's Computational Science Center has an opening for a full-time researcher in applied mathematics for energy systems, with special emphasis on scientific machine learning and uncertainty quantification. The researcher will work on computational problems arising in a variety of renewable energy applications, such as wind energy, biofuels, and power systems, that are characterized by high dimensional uncertainty, multiple model fidelities, and opportunities to learn data-driven insights and model improvements. The researcher will be expected to provide technical expertise in the design, implementation, and execution of cutting edge mathematics, algorithms, and software related to physics-informed machine learning, multifidelity uncertainty quantification, and other ‘outer loop’ problems such as optimization, sensitivity analysis and dimension reduction.
We are looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NREL who can help advance the CSSO group’s strategic emphasis on high performance computing, computational science and applied mathematics. The successful candidate will collaborate with NREL staff and researchers, other national labs, and universities on efforts to develop and apply artificial intelligence and uncertainty quantification techniques at scale to real-world problems in renewable energy research.
Job Duties and Responsibilities
Conduct independent and collaborative research in developing and implementing emerging scientific machine learning frameworks for energy systems applications, particularly relating to adversarial training techniques and invertible network architectures.
Conduct independent and collaborative research on advanced uncertainty quantification techniques for high dimensional energy systems using multifidelity approaches, advanced sampling techniques, gradient-based dimension reduction, and surrogate model development.
Author, present and assist in the preparation of journal papers, technical reports and conference proceedings on research topics.
Work with management and senior research staff to develop funding focused on applied math and advanced modeling in energy systems research.
Enable productive use of NREL HPC resources by scientific and engineering staff, particularly with respect to GPU usage for machine learning applications.
Mentor undergraduate and graduate interns on related research.
PhD in Mathematics, Engineering or related. Or, Master's Degree in Mathematics, Engineering or related and 3 or more years of experience . Or, Bachelor's Degree in Mathematics, Engineering or related and 5 or more years of experience .
Additional Required Qualifications
Demonstrates complete understanding and wide application of scientific technical procedures, principles, theories and concepts in the field. General knowledge of other related disciplines. Demonstrates leadership in one or more areas of team, task or project lead responsibilities. Demonstrated experience in management of projects. Very good technical writing, interpersonal and communication skills.
Familiarity with physical phenomena arising in renewable energy technologies, e.g. aerodynamics, combustion, battery chemistry, building HVAC systems, etc.
Familiarity with control strategies such as reinforcement learning and model predictive control techniques.
Ability to quickly adapt to new domains and translate applied math techniques from previous experience to unfamiliar settings.
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The National Renewable Energy Laboratory (NREL) is a leader in the U.S. Department of Energy’s effort to secure an environmentally and economically sustainable energy future. With locations in Golden and Boulder, Colorado, and a satellite office in Washington, D.C., NREL is the primary laboratory for research, development, and deployment of renewable energy technologies in the United States.
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