SULI - TAPS - Kliufinskyi, Viktor - 6.17.26

Argonne National Laboratory • Lemont, IL

4d • Onsite

About The Position

To understand total nationwide transportation fuel consumption, we need to have a solid understanding of the vehicles themselves. This project will analyze different data sets of vehicle registrations in the United States, comparing them to see similarities and differences. This will include both commercially acquired and open data sets, to determine if the open data sets are sufficient for future analysis. Given two data sets which nominally represent the same vehicle population, what are the differences in specific vehicles, and how would these differences translate to changes in fuel consumption and gasoline expenditures? Ideally, this project will tap into resources available for analysis using AI to generate synthetic data modeling future fleets. Please note that this is an analyst position, not a hands-on laboratory position.

Requirements

• Currently enrolled in undergraduate or graduate studies at an accredited institution.
• Graduated from an accredited institution within the past 3 months.
• Actively enrolled in a graduate program at an accredited institution.
• Must be 18 years or older at the time the appointment begins.
• Must possess a cumulative GPA of 3.0 on a 4.0 scale.
• Must be a U.S. citizen or Legal Permanent Resident at the time of application.
• Experience in a programming language for data processing and analysis (preferably Python).
• Excellent writing and presentation skills.
• Must pass a screening drug test if accepting an offer.

Responsibilities

• Analyze different data sets of vehicle registrations in the United States, comparing them to see similarities and differences.
• Determine if open data sets are sufficient for future analysis.
• Analyze differences in specific vehicles between two data sets and how these differences translate to changes in fuel consumption and gasoline expenditures.
• Tap into resources available for analysis using AI to generate synthetic data modeling future fleets.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed