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

National Laboratory of the Rockies

Ümraniye

On-site

TRY 2,167,000 - 3,466,000

Part time

4 days ago
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Benefits offered by this job

Medical insurance
Dental insurance
Vision insurance
403(b) employer match

Job summary

The National Laboratory of the Rockies (NLR) invites applications for a Graduate Year-Round Intern in Geospatial Data Science Modeling and Analysis. The role supports the Geospatial Data Science team within the Strategic Energy Analysis Center, focusing on energy deployment, big data, and geospatial modeling.

You will work with researchers to build and validate analytical solutions, conduct Python programming, and contribute to data preparation, literature reviews, and visualization, while

Qualifications

  • Minimum 3.0 GPA.
  • Undergraduate must be enrolled full-time in a bachelor’s program.
  • Graduate/PG: enrolled full-time in master’s or PhD; eligible for internship up to 1 year.

Responsibilities

  • Manipulate data sources and models for decision support and data analysis.
  • Prepare and process geospatial data with open-source tools.
  • Conduct background research on data sets and modeling approaches.
  • Develop literature reviews under supervision of researchers.
  • Program and script in Python to analyze and visualize data.
  • Work in HPC and cloud environments.

Skills

Geospatial data analysis
Data wrangling
Scientific programming
Communication skills
Independent work

Education

Bachelor’s degree in Geography/GIS/Environmental Science/CS/Engineering
Current or recent enrollment in a Master’s or PhD program

Tools

Python
QGIS
GitHub
Linux/Unix
HPC/Cloud

Job description

Posting Title

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 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 are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning 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 helps strengthen U.S. industries, support job creation, and promote 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.

Job Description

The Geospatial Data Science (GDS) team within the Strategic Energy Analysis Center at the National Laboratory of the Rockies (NLR) is seeking a 6–12-month intern to support modeling and analysis. The GDS group conducts research at the intersection of energy deployment, big data science, and geospatial modeling and visualization. Our team of researchers develops and applies geospatial algorithms and methods to evaluate the 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 under a variety of regulatory, sociopolitical, and environmental factors from local to continental scales. The successful intern candidate will support a large and diverse modeling portfolio with activities involving geospatial analyses, literature reviews, cartography, scientific programming, and tasks focused on data preparation, processing, and validation. This position will assist geospatial scientists in developing novel analytical solutions to complex challenges in energy siting, deployment, and adoption. Candidates should be interested in working in an interdisciplinary field, together with geospatial data scientists, software developers, and policy analysts, which will require excellent interpersonal and communication skills.

Duties Will Include
  • Manipulating multiple data sources, models, and software tools with scientific and engineering workflows for decision support and data analysis
  • Preparing and processing geospatial data using open-source desktop software and programmatic approaches
  • Conducting background research on data sets, current modeling approaches, and existing tools
  • Developing literature reviews under the guidance of research scientists
  • Programming and scripting in Python to conduct analysis and visualize data
  • Working in distributed computing environments including internal servers, NLR's high-performance computing (HPC) system and the Cloud
Basic Qualifications
  • Minimum of a 3.0 cumulative grade point average.
  • Undergraduate: Must be enrolled as a full-time student in a bachelor’s degree program from an accredited institution.
  • Post Undergraduate: Earned a bachelor’s degree within the past 12 months. Eligible for an internship period of up to one year.
  • Graduate: Must be enrolled as a full-time student in a master’s degree program from an accredited institution.
  • Post Graduate: Earned a master’s degree within the past 12 months. Eligible for an internship period of up to one year.
  • Graduate + PhD: Completed master’s degree and enrolled as PhD student from an accredited institution.
Please Note
  • Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
  • If selected for position, a letter of recommendation will be required as part of the hiring process.
  • Must meet educational requirements prior to employment start date.
  • Must meet educational requirements prior to employment start date.
Additional Required Qualifications
  • Must have 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 fields such as: Geography/GIS, Environmental Science, Computer Science, Engineering, or a related technical field
  • To be considered, all candidates must include in their resume and an online code repository such as GitHub with several publicly visible coding projects. Alternatively, please 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 geopandas, numpy, scipy, shapely, pyproj
  • Proficiency and experience with geospatial analysis and modeling techniques using open-source tools, such as QGIS; (please note that ESRI tools are not used in this research group)
  • Experience working with geographic transformations and projections
  • Ability to work within a dynamically evolving digital environment as a member of collaborative and often-dispersed team
  • Critical thinking skills; analytic and research skills; written and verbal communication skills
Preferred Qualifications
  • Experience with linux/unix
  • Experience with collaborative code development
  • Experience with Machine Learning
  • Experience with the Geospatial data abstraction library (GADL)
  • Experience with big geospatial data processing
Job Application Submission Window

The anticipated closing window for application submission is up to 30 days and may be extended as needed.

Annual Salary Range (based on full-time 40 hours per week)

Job Profile: / Annual Salary Range: $44,500 - $71,200

Benefits Summary

Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.

  • Based on eligibility rules
Badging Requirement

NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.

Drug Free Workplace

NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug. If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.

Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.

Reasonable Accommodations

E-Verify www.dhs.gov/E-Verify For information about right to work, click here for English or here for Spanish. E-Verify is a registered trademark of the U.S. Department of Homeland Security. This business uses E-Verify in its hiring practices to achieve a lawful workforce.

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