Graduate Intern – Artificial Learning and Intelligence for Transportation Safety and Operations

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Working at NLR

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 Artificial Learning and Intelligent Systems Group in the Computational Science Center at the National Laboratory of the Rockies (NLR) has two openings for graduate student researchers to support the Federal Highway Administration (FHWA) Office of Safety and Operations Research and Development (HRSO R&D) Super Intelligence (SI) Program.

FHWA’s HRSO R&D office is building an SI program to improve the safety and efficiency of the nation’s highway system, guided by its SI Roadmap. Running such a program requires more than research results: the office also needs to track the fast-moving SI landscape, prioritize investments, communicate with stakeholders, and turn research outcomes into practical guidance for transportation agencies.

The interns will work with NLR and FHWA colleagues on both sides of this effort: supporting the business of the AI program itself, including roadmap development, portfolio planning, and technical communication, and conducting applied AI research for traffic safety and operations.

We are looking for motivated researchers with strong AI and programming skills, an interest in transportation, and the ability to communicate technical ideas clearly to both technical and non-technical audiences. The successful candidates will collaborate with NLR researchers and FHWA program staff.

Responsibilities include:

• Conducting technology and literature scans of emerging AI methods (e.g., large language models, foundation/world models, agentic AI) and summarizing their relevance, maturity, and risks for FHWA.
• Develop and evaluate AI and machine-learning models for traffic operations and safety (e.g., traffic flow prediction, speed limit control, cooperative driving automation, detecting, predicting and explaining traffic anomalies such as non-recurrent congestion, and crashes).
• Process and integrate multimodal, multisource transportation datasets from disparate sources (e.g., sensor, video, trajectory, and incident data) and validate findings against field data.
• Write well-documented Python code and run workflows on NLR’s high-performance computing systems as needed.
• Document methodologies and results, and contribute to technical reports, presentations, and publications.
• Support the HRSO R&D SI program beyond research, including:
  • Help Organize and characterize the SI program’s project portfolio (e.g., inventories of projects, datasets, models, and outcomes) to inform planning and investment decisions.
  • Preparing briefings, white papers, fact sheets, and presentation materials for FHWA leadership, stakeholders, and workshops.
  • Supporting program coordination, such as tracking tasks and progress, preparing meeting materials, and documenting decisions and action items.

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Basic Qualifications

* Must meet educational requirements prior to employment start date.

Additional Required Qualifications

• Proficiency in Python for data analysis and machine learning (e.g., pandas, NumPy, scikit-learn, PyTorch, or TensorFlow).
• Hands-on experience developing or applying modern AI methods (e.g., deep learning or large language models).
• Coursework or project experience in transportation or traffic engineering, computer science, or data science.
• Excellent written and verbal communication skills, including the ability to summarize technical work for non-technical audiences, and the ability to work on a multi-organization team.

Preferred Qualifications

• Experienced in optimization (including quantum computing), game theory, statics, and traffic flow theory.
• Experience with traffic operations or safety data (e.g., detector, probe, trajectory, video, or crash data) or with Transportation Management Center (TMC) operations.
• Familiarity with causal inference, anomaly detection, meta-learning, or synthetic data generation.
• Experience building applications with large language models (e.g., retrieval-augmented generation, agents, or automated document analysis).
• Experience with research program support such as technology assessments, roadmaps, literature reviews, or policy/briefing documents.
• Familiarity with responsible/trustworthy AI concepts (e.g., AI risk management, model transparency, data governance) in a public-sector context.
• Prior publications or presentations in transportation or computer science venues.
• Experience running code on HPC systems and using Git.
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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)

NLR takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.

Benefits Summary

* Based on eligibility rules

Badging 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.

Submission Guidelines

Please note that in order to be considered an applicant for any position at NLR 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.

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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

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About Us

The National Laboratory of the Rockies (NLR) is the U.S. Department of Energy's primary national laboratory for critical minerals, energy innovation, and energy security. With locations in Golden and Boulder, Colorado, Fairbanks, Alaska, and a satellite office in Washington, D.C., NLR bridges foundational research with practical applications to develop and bring to scale new energy materials and technologies that lower energy costs, drive economic growth, and deliver abundant and reliable energy.

NLR is subject to Department of Energy (DOE) access restrictions. All candidates must be authorized to access the facility per DOE rules and guidance within a reasonable time frame for the specified position in order to be considered for an interview and for hiring. DOE rules for site access during the interview process depend on whether the candidate is interviewed on-site, off-site, or via telephone or videoconference. 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. Additionally, DOE contractor employees are prohibited from participating in certain Foreign Government Talent Recruitment Programs (FGTRPs). If a candidate is currently participating in an FGTRP, they will be required to disclose their participation after receiving an offer of employment and may be required to disengage from participation in the FGTRP prior to commencing employment. Any offer of employment is conditional on the ability to obtain work authorization and to be granted access to NLR by the Department of Energy (DOE).

Drug Free Workplace

NLR is committed to maintaining a drug-free workplace in accordance with 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.