Posting Title

Researcher II/III-Geospatial Science

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Location

CO - Golden

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Position Type

Regular

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Hours Per Week

40

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Working at NREL

The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for research and development of renewable energy and energy efficiency technologies.

From day one at NREL, you’ll connect with coworkers driven by the same mission to save the planet. By joining an organization that values a supportive, inclusive, and flexible work environment, you’ll have the opportunity to engage through our ten employee resource groups, numerous employee-driven clubs, and learning and professional development classes.

NREL supports inclusive, diverse, and unbiased hiring practices that promote creativity and innovation. By collaborating with organizations that focus on diverse talent pools, reaching out to underrepresented demographics, and providing an inclusive application and interview process, our Talent Acquisition team aims to hear all voices equally. We strive to attract a highly diverse workforce and create a culture where every employee feels welcomed and respected and they can be their authentic selves.

Our planet needs us! Learn about NREL’s critical objectives, and see how NREL is focused on saving the planet.

Note: Research suggests that potential job seekers may self-select out of opportunities if they don't meet 100% of the job requirements. We encourage anyone who is interested in this opportunity to apply. We seek dedicated people who believe they have the skills and ambition to succeed at NREL to apply for this role.

Job Description

The Geospatial Data Science (GDS) Group in NREL’s Strategic Energy Analysis Center is seeking a research scientist to conduct analysis of spatial and temporal data to help solve real-world energy system problems and advance renewable energy deployment. The GDS Group conducts research at the intersection of renewable energy deployment, big data science, and geospatial modeling and visualization. Our team of scientists develops and applies geospatial algorithms and methods to evaluate the resource, technical, and economic potential for renewable energy technologies, principally wind and solar but also geothermal, water power, hydrogen, and bioenergy. Our work aims to put actionable insights in the hands of decision-makers, industry partners, stakeholders, and the broader public. Geospatial modeling at NREL enables detailed techno-economic assessment and power systems modeling of renewable energy resources under a variety of regulatory, sociopolitical, and environmental factors from local to continental scales.

This position will support the growing research portfolio of the GDS Group, creating new capabilities to analyze scenarios and design solutions for complex challenges in renewable energy. The position will support clients within NREL and external to the laboratory. The successful candidate will develop novel analytical solutions to advance the state of research in broad geographic scale representation of spatial drivers of renewable energy deployment and the evolution of our power system. This position requires a strong applied and theoretical background in spatiotemporal methods applied in social, ecological, and topographical modeling contexts, as well as understanding of complex systems analysis and scenario modeling frameworks.

Duties will include:

o   Integrating multiple data sources, models, and software tools with scientific and engineering workflows for data analysis and decision support. These workflows will include the use of distributed parallel computing and utilization of both cloud and high-performance computing (HPC) resources.

o   Strong scientific programming and algorithm development skills and demonstrated use of Python for modeling and analysis of large, complex data sets. Experience with Linux operating systems and bash scripting preferred.

o   Conducting and leading analysis using the Renewable Energy Potential (reV) model to develop renewable energy supply curves. Wo