Graduate; Year-Round Intern: Geospatial Data Science Modeling and Analysis

Graduate (Year-Round) Intern:
Geospatial Data Science Modeling and Analysis

Location:

CO - Golden

Position Type:
Intern (Fixed Term)

Hours per Week: 40

Working at NLR: NLR is located at the foothills of the Rocky Mountains in Golden, Colorado and is the nation's primary laboratory for energy systems research and development. Join the National Laboratory of the Rockies (NLR), where world‑class scientists, engineers, and experts accelerate energy innovation through breakthrough research and systems integration. From mission to collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future.

Spanning from foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost‑effective energy solutions. Our work strengthens U.S. industries, supports job creation, and promotes national economic growth.

At NLR, you'll find a mission‑driven environment supported by state‑of‑the‑art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities and a competitive benefits package designed to support your career and well‑being.

The Geospatial Data Science (GDS) team within the Strategic Energy Analysis Center at NLR seeks a 6–12‑month intern to support modeling and analysis. The group's research intersects energy deployment, big data science, and geospatial modeling and visualization. Our researchers develop and apply geospatial algorithms and methods to evaluate deployment potential for energy technologies, including geothermal, hydropower, transmission, bioenergy, wind, and solar.

Geospatial modeling at NLR enables detailed techno‑economic assessment, capacity expansion, and power systems modeling of energy resources across regulatory, sociopolitical, and environmental contexts from local to continental scales.

• Manipulate multiple data sources, models, and software tools with scientific and engineering workflows for decision support and data analysis.
• Prepare and process geospatial data using open‑source desktop software and programmatic approaches.
• Conduct background research on datasets, current modeling approaches, and existing tools.
• Develop literature reviews under the guidance of research scientists.
• Program and script in Python to conduct analysis and visualize data.
• Work in distributed computing environments, including internal servers, NLR's high‑performance computing (HPC) system, and the Cloud.

• Minimum cumulative GPA of 3.0.
• Undergraduate: must be enrolled full‑time in a bachelor’s degree program at an accredited institution.
• Post‑Undergraduate: earned a bachelor’s degree within the past 12 months; eligible for up to one year of internship.
• Graduate: must be enrolled full‑time in a master’s degree program at an accredited institution.
• Post‑Graduate: earned a master’s degree within the past 12 months; eligible for up to one year of internship.
• Graduate + PhD: completed master’s degree and currently enrolled as a PhD student at an accredited institution.

• Completed a bachelor’s degree and either be enrolled in or recently graduated from a master’s degree or currently enrolled in a PhD program in Geography/GIS, Environmental Science, Computer Science, Engineering, or a related technical field.
• Include in your resume and an online code repository such as Git Hub with several publicly visible coding projects, or explain in your cover letter why your application does not include a code repository and/or several examples of previous projects.
• Demonstrated experience working independently with programming skills in Python and libraries such as Geo Pandas, Num Py, Sci Py, Shapely, and PyProj.
• Proficiency and experience with geospatial analysis and modeling techniques using open‑source tools such as QGIS (ESRI tools are not used).
• Experience with geographic transformations and projections.
• Ability to work within a dynamically evolving digital environment as a member of collaborative…