Argonne National Laboratory in the Intelligent Vehicle Control group within Argonne’s Vehicle and Mobility Systems (VMS) department seeks a highly qualified researcher to lead advanced controls development for Connected and Automated Vehicles (CAVs). The successful candidate will drive innovation in control and optimization, scenario generation, and multi-resolution modeling, with a focus on real-world deployment and experimental validation. This position will play a key role in advancing Argonne’s mission of energy-efficiency, affordability and increased mobility in vehicle and transportation systems.
The selected candidate will join a multidisciplinary team at the forefront of CAV research, working on projects that span from theoretical control development to real-world experimentation. The primary responsibilities include:
Leading the development and refinement of advanced control algorithms for CAVs, leveraging state-of-the-art techniques such as Koopman Operator theory, model-predictive control, optimal control theory and AI-based methods.
Designing and implementing CAV models, including CAV-tailored scenarios and, using a multi-resolution approach, with tools such as Argonne’s RoadRunner, SUMO and POLARIS.
Advancing traffic flow modeling capabilities within RoadRunner, with a focus on scalable, data-driven, and AI-enhanced approaches.
Overseeing the deployment of control algorithms to experimental hardware platforms, supporting hardware-in-the-loop and on-road testing.
Collaborating with internal and external partners to ensure seamless integration of control and simulation tools, and to support large-scale case studies and field experiments.
Documenting research outcomes, publishing in high-impact venues, and presenting findings to stakeholders and the broader scientific community.
Mentoring junior staff and contributing to the strategic direction of the Intelligent Vehicle Control group.
Position Requirements
Ph.D. in Mechanical, Electrical, Civil, or related Engineering field, with a focus on connected and automated vehicle systems, control, or transportation systems.
At least five years of research experience in CAV control, optimization, or traffic simulation.
Demonstrated expertise in advanced control theory and optimization, including but not limited to: Koopman Operator methods, model-predictive control, optimal control, reinforcement learning, and AI for controls.
Proven experience in CAV scenario generation, multi-resolution modeling, and integration with large-scale simulation platforms (e.g., POLARIS).
Strong background in traffic flow modeling, with hands-on experience developing and extending simulation tools such as RoadRunner.
Proficiency in programming languages and environments relevant to CAV research (e.g., Matlab, Python, C++), and experience with model development in Simulink.
Experience deploying control algorithms to experimental hardware, including hardware-in-the-loop and real-world vehicle platforms.
Excellent written and oral communication skills, with a strong publication record and experience presenting to diverse audiences.
Demonstrated ability to work collaboratively in multidisciplinary teams and mentor junior researchers.
Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
This position requires an on-site presence at the Argonne campus in Lemont, Illinois.
Job Family
Research Development (RD)Job Profile
Computational Science 3Worker Type
RegularTime Type
Full timeThe expected hiring range for this position is $116,250.00 - $181,350.00.Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
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Skills Required
- Ph.D. in Mechanical, Electrical, Civil, or related Engineering field focused on CAV systems, control, or transportation systems.
- At least five years of research experience in connected and automated vehicle (CAV) control, optimization, or traffic simulation.
- Demonstrated expertise in advanced control theory and optimization, including Koopman Operator methods, model-predictive control, optimal control, reinforcement learning, and AI for controls.
- Proven experience in CAV scenario generation, multi-resolution modeling, and integration with large-scale simulation platforms (e.g., POLARIS).
- Strong background in traffic flow modeling and hands-on experience developing/extending simulation tools such as RoadRunner.
- Proficiency in Matlab, Python, and C++, and experience with model development in Simulink.
- Experience deploying control algorithms to experimental hardware platforms, including hardware-in-the-loop and real-world vehicle testing.
- Excellent written and oral communication skills, strong publication record, and experience presenting to diverse audiences.
- Demonstrated ability to work collaboratively in multidisciplinary teams and mentor junior researchers.
- On-site presence at the Argonne campus in Lemont, Illinois.
- Ability to model Argonne's Core Values (Impact, Safety, Respect, Integrity, Teamwork).
- Successful completion of background check and potential requirement to obtain government access authorization.
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Argonne National Laboratory, one of the U.S. Department of Energy's national laboratories for science and engineering research, employs 3,400 employees, including 1,400 scientists and engineers, three-quarters of whom hold doctoral degrees.
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