Graduate (Summer or Year-Round) Intern for the Application of AI/ML for Mobility Research and Analysis

View full description

Requirements:

• Experience across a large variety of current AI/ML tools.
• Fundamental knowledge of transportation engineering/planning, ideally including expertise in current mobility standards and practices as they pertain to automated mobility and traffic engineering.

Responsibilities:

• Develop a sentiment analysis tool using data harvested from social media on the attitudes and user benefits and issues associated with rapidly scaling vehicle automation such as Waymo Robo-taxis and other automated public mobility initiatives.
• Research use of AI/ML to augment or supplant traditional stochastic and Bayesian methods for multi-track sensor fusion, and compare and contrast it with more algorithmic approach using Bayesian association and Kalman filtering, particularly with respect to object permanence through the field of observation.
• Assist NREL researchers on applications of ML/AI in various fields of transportation infrastructure and vehicle engineering, with particular emphasis on DOT functions and responsibilities.
• Experiment with large language models and their associated tools to increase the productivity of research within the MIE team including literature review, drafting of initial results, and refinement of final product.
• Document methods and assumptions and assist with preparing peer-reviewed publications along with high-quality technical reports and presentations.