Graduate Intern

About the Role

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.

Key Responsibilities

• 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.

Requirements & Tech Stack

• 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.

Qualifications

• Minimum of a 3.0 cumulative grade point average.
• Must be enrolled as a full-time student in a master’s degree program from an accredited institution, or have earned a master’s degree within the past 12 months and be eligible for an internship period of up to one year.
• Alternatively, must be enrolled as a PhD student from an accredited institution, or have completed a master’s degree and be enrolled as a PhD student from an accredited institution.
• Applicants are responsible for uploading official or unofficial school transcripts as part of the application process.
• A letter of recommendation will be required as part of the hiring process if selected for the position.
• Must meet educational requirements prior to employment start date.