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

National Laboratory of the Rockies • Golden, CO

9d • $44,500 - $71,200 • Onsite

About The Position

The Advanced Computing Solutions Group in the Computational Science Center at the National Laboratory of the Rockies (NLR) is seeking a graduate student researcher to model connected and automated vehicle communications, emphasizing data analysis and machine learning. Vehicle-to-everything (V2X) communication enables vehicles to communicate with one another and roadside infrastructure, supporting cooperative driving automation. Because field-testing V2X at the scale needed to assess traffic impacts is cost-prohibitive, simulation provides a practical approach. The intern will work with NLR colleagues to analyze field V2X data and develop V2X models for simulation.

The intern will analyze large-scale field-test logs, help calibrate and validate microscopic LTE-V2X communication models, and develop machine-learning surrogate models from simulation data. The role includes writing documented Python code, running workflows on NLR high-performance computing systems, and contributing to reports, presentations, and publications.

This is a fixed-term internship located in Golden, Colorado, with a stated schedule of 40 hours per week. The position is for a graduate student or a recent graduate who meets the education eligibility requirements. The annual salary range for full-time, 40-hour-per-week work is $44,500–$71,200. Benefits may include medical, dental, and vision insurance, a 403(b) Employee Savings Plan with employer match, and sick leave where required by law; eligibility rules apply. Interns assigned for more than six months are subject to the stated PIV card and background investigation requirement.

Requirements

• Minimum cumulative GPA of 3.0.
• Meet one of the stated education eligibility paths: be enrolled full-time in an accredited bachelor's program; have earned a bachelor's degree within the past 12 months; be enrolled full-time in an accredited master's program; have earned a master's degree within the past 12 months; or have completed a master's degree and be enrolled as a PhD student at an accredited institution.
• Proficiency in Python for data analysis, such as pandas, NumPy, and Matplotlib.
• Experience applying machine learning to scientific or engineering data, such as with 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 ability to work on a multi-laboratory team.
• Upload official or unofficial school transcripts as part of the application process.
• Meet educational requirements before the employment start date.
• If selected, provide a letter of recommendation as part of the hiring process.
• Be able to obtain and maintain a federal PIV card as required for internship assignments extending beyond six months, including a favorable background investigation.
• Pass a pre-employment drug test if offered employment.

Nice To Haves

• Familiarity with V2X technologies such as C-V2X/LTE-V2X, 5G NR sidelink, or DSRC, SAE J2735 messages, or wireless channel modeling.
• Experience with traffic simulation tools such as Aimsun, SUMO, or VISSIM, or connected and automated vehicle modeling such as car-following or cooperative adaptive cruise control.
• Experience with co-simulation frameworks such as HELICS or network simulators such as ns-3 or OMNeT++/Veins.
• Experience with surrogate modeling or reduced-order modeling of simulation output.
• Experience running code on high-performance computing systems and using Git.

Responsibilities

• Clean, process, and analyze large-scale V2X field-test logs to characterize communication performance, including packet delivery ratio, latency, and range, by distance, device density, and environmental conditions.
• Help calibrate and validate microscopic LTE-V2X communication models against field-test data.
• Develop and train machine-learning surrogate models of V2X behavior using large simulation datasets, and evaluate their accuracy and computational efficiency.
• Write well-documented Python code and run workflows on NLR high-performance computing systems.
• Document results and contribute to technical reports, presentations, and publications.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 403(b) Employee Savings Plan with employer match
• Sick leave where required by law
• Some positions may be eligible for relocation expense reimbursement
• Potential performance-, merit-, and achievement-based awards with a monetary component; eligibility is not guaranteed

What This Job Offers

• Career Level: Intern