Graduate Intern – Connected Vehicle (V2X) Communications Modeling and Traffic Simulation

Posting TitleGraduate Intern – Connected Vehicle (V2X) Communications Modeling and Traffic Simulation

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LocationCO - Golden

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Position TypeIntern (Fixed Term)

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Hours Per Week40

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Working at NLRNLR 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 Advanced Computing Solutions Group in the Computational Science Center at the National Laboratory of the Rockies (NLR) has an opening for a graduate student researcher in connected and automated vehicle communications modeling, with an emphasis on data analysis and machine learning.

 

Vehicle-to-everything (V2X) communication lets vehicles talk to each other and to roadside infrastructure, and it is the backbone of cooperative driving automation. Field-testing V2X at the scale needed to see its effect on traffic would take hundreds of roadside units and thousands of equipped vehicles, which is cost-prohibitive, so simulation is the practical path.

 

The intern will work with NLR colleagues to analyze field V2X data and develop new V2X models for simulation.

 

We are looking for a motivated researcher with strong data and programming skills and an interest in the mission of NLR. The successful candidate will collaborate with NLR researchers.

 

Responsibilities include:

• Clean, process, and analyze large-scale V2X field-test logs to characterize communication performance (e.g., packet delivery ratio, latency, and range) as a function of distance, device density, and environmental conditions.
• Help calibrate and validate microscopic LTE-V2X communication models against the field-test data.
• Develop and train machine-learning surrogate (approximate macroscopic) models of V2X behavior from large sets of simulation data, and evaluate their accuracy and computational efficiency.
• Write well-documented Python code and run workflows on NLR’s high-performance computing systems.
• Document results and contribute to technical reports, presentations, and publications.

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Basic QualificationsMinimum 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

• Proficiency in Python for data analysis (e.g., pandas, NumPy, Matplotlib).
• Experience applying machine learning to scientific or engineering data (e.g., scikit-learn, PyTorch, or TensorFlow).
• Coursework or project experience in at least one of: wireless communications or networking, transportation or traffic engineering, or data science.
• Excellent written and verbal communication skills and the ability to work on a multi-laboratory team.

Preferred Qualifications

• Familiarity with V2X technologies (C-V2X/LTE-V2X, 5G NR sidelink, or DSRC), SAE J2735 messages, or wireless channel modeling.
• Experience with traffic simulation (Aimsun, SUMO, or VISSIM) or connected and automated vehicle modeling (e.g., car-following or cooperative adaptive cruise control).
• Experience with co-simulation frameworks (e.g., HELICS) or network simulators (e.g., ns-3, OMNeT++/Veins).
• Experience with surrogate modeling or reduced-order modeling of simulation output.
• 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)Job Profile: / Annual Salary Range: $44,500 - $71,200

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 SummaryBenefits 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 RequirementNLR 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.

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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