Postdoctoral Appointee: Researcher in Connected and Automated Vehicle Systems
- Argonne National Laboratory
- Location: Argonne, IL
- Job Number: 7109291 (Ref #: 414001)
- Posting Date: Aug 30, 2022
- Application Deadline: Open Until Filled
Assist in developing and administering experimental evaluations and conducting analysis of chassis dynamometer and on-road vehicle experiments exploring connected and automated vehicle technologies. These efforts will involve implementing unique hardware and software using open source, and off-the shelf solutions directly on advanced technology vehicles and coupled instrumentation systems. Programming languages and analysis tools along with strong knowledge of vehicle operation and testing methodologies will be leveraged to support activities in this position. Collaborate with various research groups within the Center for Transportation Research (CTR) to develop and execute experimental activities placing real vehicles into simulated environments and developing related experimental capabilities and test procedures. Collaborations will focus on utilizing existing capabilities and developing new systems, enabling cutting- edge experimental activities in vehicle automation and connectivity.
Research existing industry leading CAVs test methodologies and assist in developing new methodologies with particular emphasis on evaluating energy consumption and operation for vehicles equipped with various levels of powertrain electrification, connectivity, and automation.
Give technical presentations at workshops and conferences. Develop and submit abstracts/papers for professional conferences and/or journals. Research new industry-leading engineering tools to ensure research activities remain industry-relevant. Adopt and refine the latest cutting-edge processes and tools. Continue to gain knowledge in the transportation field, particularly with advanced vehicle technology. Maintain current knowledge of the automotive industry and its interaction with DOE and other government agencies.
Safety, Security, and Environmental Protection: All activities, as they apply to work performed by self or by personnel under supervision, will be executed in compliance with ES&H and security responsibilities established by Argonne National Laboratory's ES&H policies, Safeguards and Security policies, work rules, and safe practices.
Knowledge in vehicle experimentation, specifically XIL or VIL, ensuring selection of appropriate instrumentation and methodologies for testing to provide accurate and comprehensive experimental results on vehicle systems.
Knowledge of data processing, analysis of vehicle systems.
Knowledge of vehicle automation, including perception system sensor technology such as radar, lidar, vision sensing and sensor data processing.
Knowledge of automated longitudinal and lateral control features, including algorithm development and test methodologies in simulation and dynamometer/track environments.
Knowledge of transportation technologies, energy use, and environmental impacts of transportation.
Skilled in ROS / Python / C++, or LabVIEW / DIAdem
Skills in Python database interaction, information extraction, and report/plot generation.
Skills using MS Office including Excel, Word and PowerPoint.
Skilled in oral and written communications at all levels of the organization.
Skilled in time management, organization, and working on several projects simultaneously.
Skills in contribution to project/program completion, exercising significant impact on program results.
Desire for continued learning in, and general interest of automotive technologies and their associated energy use.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
What will put you ahead:
Direct experience with FHWA CARMA Platforms.
Experience with traffic systems hardware and connectivity (C-V2X preferred) systems.
Direct involvement managing or operating Vehicle-in-the-Loop experiments on a chassis dynamometer or on-track.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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