R&D Associate Staff - Applied Research for Mobility Systems

Oak Ridge National Laboratory•KNOXVILLE, TN

31d•Onsite

About The Position

The Applied Research for Mobility Systems (ARMS) Group at Oak Ridge National Laboratory (ORNL) is seeking applicants for an R&D Associate Staff position focused on connected and automated vehicles, advanced driver assistance systems, vehicle-in-the-loop simulation, and artificial intelligence for transportation. The successful candidate will develop testing, simulation, and data-driven methods to evaluate the energy, safety, and operational performance of emerging vehicle technologies. Key activities include advancing VIL and HIL capabilities, integrating vehicle and sensor systems, developing standardized test procedures, and applying AI to scenario generation and system evaluation. The ideal candidate will have a strong background in vehicle systems and controls, real-time simulation, experimental integration, and artificial intelligence. The ARMS group is within the Vehicle and Mobility Systems Research (VMSR) Section of the Buildings and Transportation Science Division (BTSD). The VMSR Section focuses on accelerating the development of advanced vehicles and complex mobility systems through advances in connected and autonomous vehicle technologies, communications, systems integration, and decision science. The objective of the ARMS Group is to provide leadership in advanced anything-in-the-loop research to accelerate the development of transportation technologies from component-level to full vehicles to traffic networks. Research in ARMS group includes: Identify, collect, and process real-world data, such as road network information (geographic information systems data), traffic density and flow, signal timing plan, fleet population mix, routing, etc., and develop realistic scenarios in traffic microsimulation software and virtual environment/vehicle simulators. Establish detailed light-duty (LD), medium-duty (MD), and/or heavy-duty (HD) vehicle models with emerging mobility technology capabilities, such as connected and automated vehicles (CAVs), cooperative driving automation (CDA), and electrified vehicles. Develop simulations of these vehicles operating in diverse traffic, geographies, and environments to assess impact of emerging mobility technologies on the transportation system. Development and implementation of advanced software-in-the-loop (SIL) and hardware-in-the-loop (HIL) systems to support advanced powertrain, CAV and CDA control system design, validation, and verification.

Requirements

A Ph.D. in Mechanical Engineering, Automotive Engineering, Electrical Engineering, Computer Engineering, Robotics, or a related field.
At least one year of relevant research or professional experience in vehicle systems, connected and automated vehicles, ADAS, controls, robotics, or transportation systems. Relevant graduate research experience may be considered.
Experience developing or using VIL, HIL, software-in-the-loop, or other real-time simulation and testing environments.
Experience with vehicle dynamics, feedback control, vehicle communication interfaces, drive-by-wire systems, or related vehicle-control technologies.
Proficiency in Python and MATLAB/Simulink for modeling, data processing, algorithm development, or experimental control.
Experience applying one or more artificial intelligence or data-driven methods, such as deep learning, computer vision, time-series analysis, generative modeling, reinforcement learning, or sensor fusion.
Demonstrated ability to develop and integrate experimental hardware and software systems.
Demonstrated record of technical publications, presentations, or sponsor-facing research reports.
Strong written and verbal communication skills and the ability to work effectively in multidisciplinary research teams.

Nice To Haves

Experience developing and validating VIL or HIL test methods for production ADAS or automated-driving systems, including closed-loop vehicle testing and sensor stimulation.
Experience integrating real-time simulation, vehicle-control, and traffic-simulation platforms such as dSPACE, CARLA, SUMO, or comparable tools.
Experience applying machine learning, computer vision, time-series analysis, generative AI, or sensor fusion to vehicle trajectories, perception modeling, edge-case generation, or transportation safety research.
Experience designing experiments and evaluating correlation among laboratory, closed-course, simulation, and real-world results.
Experience leading multidisciplinary, sponsor-funded research and coordinating with government agencies, universities, industry partners, vendors, or external test facilities.
A demonstrated record of technical publications, proposal development, research partnerships, and mentoring students or junior researchers.
A self-directed and collaborative approach, with the ability to manage multiple priorities and adapt to evolving research needs.

Responsibilities

Develop standardized testing and evaluation methods to quantify the energy, safety, and operational performance of connected and automated vehicle and ADAS technologies.
Design and conduct vehicle-in-the-loop and hardware-in-the-loop experiments that integrate production vehicles, dynamometers, vehicle controllers, virtual environments, and radar, camera, positioning, or other sensor-stimulation systems.
Develop closed-loop control, system synchronization, and validation methods to ensure laboratory testing accurately represents closed-course and real-world vehicle behavior.
Apply artificial intelligence, machine learning, computer vision, time-series modeling, and generative methods to create realistic vehicle trajectories, model perception errors, and identify safety-critical scenarios and edge cases.
Develop scalable vehicle and traffic simulation, experimental automation, and data-analysis tools using Python, MATLAB/Simulink, C/C++, and platforms such as dSPACE, CARLA, and SUMO.
Lead experimental planning and system integration activities, including equipment selection, vehicle preparation, safety considerations, and coordination with external test facilities, vendors, universities, and other research partners.
Document research methods and software; prepare sponsor deliverables, technical reports, peer-reviewed publications, and presentations; contribute to research proposals; and mentor students and junior researchers.
Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.