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

National Laboratory of the Rockies • Golden, CO

7d • Onsite

About The Position

The Advanced Computing Solutions Group in the Computational Science Center at the National Laboratory of the Rockies (NLR) seeks a graduate student researcher to work on connected and automated vehicle communications modeling, with an emphasis on 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 large-scale field testing is cost-prohibitive, simulation is a practical way to study V2X effects on traffic. The intern will collaborate with NLR researchers to analyze field V2X data and develop new V2X models for simulation.

The role involves processing field-test logs, calibrating and validating communication models, developing machine-learning surrogate models, and evaluating their accuracy and efficiency. The intern will write documented Python code, run workflows on NLR high-performance computing systems, and contribute to technical reports, presentations, and publications.

The position is based in Golden, Colorado, and is a fixed-term internship of up to one year. It is listed at 40 hours per week. The annual salary range is $44,500–$71,200. Benefits include medical, dental, and vision insurance; a 403(b) Employee Savings Plan with employer match; and sick leave where required by law. Some positions may be eligible for relocation reimbursement, and employees may be eligible for performance-, merit-, or achievement-based awards. Intern assignments extending beyond six months are subject to DOE access restrictions and the requirement to obtain and maintain a federal PIV card, including a favorable background investigation. Employment is contingent on passing a pre-employment drug test.

Requirements

• Minimum cumulative GPA of 3.0.
• Must meet one of the stated student eligibility paths: be enrolled full-time in an accredited bachelor's degree program; have earned a bachelor's degree within the past 12 months; be enrolled full-time in an accredited master's degree 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.
• Eligible for an internship period of up to one year.
• 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.
• Must meet educational requirements before the employment start date.
• Applicants must upload official or unofficial school transcripts; a letter of recommendation will be required if selected.

Nice To Haves

• 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, 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 HPC 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, based on 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 (approximate macroscopic) 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.
• Collaborate with NLR researchers and colleagues.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 403(b) Employee Savings Plan with employer match
• Sick leave where required by law
• Potential eligibility for performance-, merit-, and achievement-based awards with a monetary component
• Some positions may be eligible for relocation expense reimbursement

What This Job Offers

• Career Level: Intern