Postdoctoral Research Associate in AI

About the Role

The Applied Research for Mobility Systems Group at Oak Ridge National Laboratory (ORNL) is seeking highly motivated Postdoctoral Research Associates to conduct cutting-edge research in transportation artificial intelligence, digital twins, intelligent transportation systems, cooperative driving automation, and energy-efficient mobility. The selected candidates will work with multidisciplinary teams to develop, validate, and demonstrate advanced mobility technologies using large-scale transportation data, high-fidelity simulation, vehicle-in-the-loop testing, and real-world field deployments. The positions will advance data-driven and AI-enabled mobility systems that connect vehicle-level automation with corridor- and regional-level transportation operations. A central emphasis will be developing, testing, and deploying connected-vehicle technologies and smart-infrastructure control in real-world settings to improve the energy efficiency of mobility systems. The research will integrate multimodal transportation data, digital twins, vehicle dynamics and energy models, AI-based control and optimization, connected infrastructure, vehicle-in-the-loop and dynamometer testing, and field demonstrations to create scalable solutions for passenger, transit, and freight vehicle operations. Candidates are not expected to have expertise in every area. We welcome applicants with strong depth in one or more relevant areas and interest in collaborating across disciplines.

Key Responsibilities

• Develop AI-ready datasets, digital twins, predictive models, and representative scenarios using multimodal transportation, vehicle, infrastructure, fleet, and energy data.
• Develop AI-based control and optimization methods for connected vehicles and smart infrastructure, including eco-driving, cooperative driving automation, signal control and priority, routing, and resilient transportation operations.
• Integrate research algorithms with vehicle and energy models, simulation platforms, vehicle hardware, connected infrastructure, and traffic control systems.
• Plan and conduct testing and evaluation through simulation, software- and hardware-in-the-loop, vehicle-in-the-loop, dynamometer, and on-road field experiments.
• Deploy and demonstrate research technologies in real-world environments in collaboration with public agencies, transportation operators, national laboratories, universities, and industry partners.
• Evaluate impacts on energy efficiency, mobility, reliability, and resilience at the vehicle, corridor, and network levels.
• Produce reusable research software, datasets, models, evaluation tools, and deployment guidance; publish findings and present research at technical conferences.
• Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service.

Requirements & Tech Stack

• Ph.D. in civil engineering, mechanical engineering, computer science, electrical engineering, industrial and systems engineering, operations research, robotics, data science, or a closely related field.
• Demonstrated research experience in at least one of the following areas: vehicle dynamics simulation; modeling and control of connected and automated vehicles; AI or machine learning for transportation; traffic modeling, control, routing, or optimization; transportation digital twins and simulation; vehicle energy modeling; heavy-duty vehicle operations; intelligent transportation systems.
• Proficiency in Python and experience with at least one additional programming, modeling, or data-analysis environment such as C++, MATLAB or Simulink.
• Experience developing computational models or data-driven methods and evaluating them using simulation, experimental, or real-world data.
• Strong analytical and problem-solving skills and the ability to work both independently and collaboratively in a multidisciplinary research environment.
• Excellent written and verbal communication skills, demonstrated through scholarly publications, technical reports, presentations, or comparable research products.

Preferred Qualifications

• Experience with traffic simulation, vehicle simulation, or digital-twin platforms such as SUMO, VISSIM, Aimsun, CARLA, CarMaker, MATLAB, or Simulink.
• Experience with AI and machine-learning methods, including reinforcement learning, multi-agent systems, graph neural networks, spatiotemporal models, or Transformers, using PyTorch or a comparable framework.
• Experience applying LLMs, VLMs, or agentic AI to vehicle and infrastructure control, multimodal reasoning, scenario generation, planning, or transportation operations.
• Experience with traffic prediction, routing, signal control and priority, network optimization, or regional transportation operations, including relevant systems and interfaces such as NTCIP, NEMA, SAE J2735, or ATMS.
• Experience integrating and analyzing multimodal transportation data, including trajectory, detector, video, LiDAR, connected-vehicle, transit, or fleet data.
• Experience with vehicle hardware, control-system integration, and cooperative driving automation, including software-, hardware-, or vehicle-in-the-loop testing, dynamometer evaluation, or on-road experiments.
• Knowledge of trustworthy AI, uncertainty quantification, safety guardrails, or evaluation under disruptive and out-of-distribution conditions.
• Experience developing reproducible research software and collaborating with public agencies, transportation operators, industry partners, or multidisciplinary research teams.

Special Requirements

• Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment.
• The appointment length will be up to 24 months with the potential for extension.
• For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required.
• All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card.
• This requires a favorable post-employment background investigation, including a declaration of illegal drug activities within the last year.
• For foreign national candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination.
• Submit two letters of reference.