Postdoctoral Research Associate

Job Title

Postdoctoral Research Associate

Texas A&M University - Corpus Christi

College of Engineering - ENGR

Commensurate

Corpus Christi, Texas

Staff

TAMU-CC is a dynamic university designated as both a Hispanic-Serving Institution (HSI) and Minority-Serving Institution (MSI) with approximately 11,000 students from 47 states and 54 foreign nations. We employ over 1,400 full-time and 2,000 part-time Islanders (including students/GAs). The University attracts highly talented faculty and staff and offers an array of undergraduate and graduate degrees, including doctoral programs. As a member of the Texas A&M University System, TAMU-CC benefits from a range of resources, increased visibility and influence, and opportunities to collaborate in mutually beneficial ways with peers across member institutions and associated agencies.

TAMU-CC’s beautiful campus is located on a 240-acre island on Corpus Christi Bay and was ranked #1 College by the Sea by Best College Reviews. Our natural setting is enhanced by its modern, attractive, and state-of-the-art classroom buildings and support facilities.

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The College of Engineering and Computer Science at Texas A&M University–Corpus Christi (TAMU-CC) invites applications for a Postdoctoral Research Associate to advance the state of the art in safe, efficient, and uncertainty-aware algorithms for autonomous driving systems. The position focuses on developing robust decision-making, planning, and control methods that address uncertainty arising from perception, vehicle dynamics, and complex real-world environments. Research themes include model predictive control, reinforcement learning, reachability analysis, uncertainty quantification, and safety-critical visual reasoning.

This position offers the opportunity to collaborate with an interdisciplinary team of researchers in a highly supportive and innovative environment. The appointment is for one year, with the possibility of renewal based on performance and available funding.

Research: 50%

• Conduct research on safe, efficient, and uncertainty-aware algorithms for autonomous driving systems.
• Research themes include:
  • Safe and efficient decision-making and control for autonomous vehicles.
  • Model Predictive Control (MPC), Reinforcement Learning (RL), and reachability analysis.
  • Uncertainty quantification and propagation in planning and control.
  • 3D scene understanding, cooperative perception, and safety-critical visual reasoning.
• Develop theoretical and computational methods to advance these areas.
• Publish research results in peer-reviewed journals and present findings at conferences.

Programming, Simulation, and Experimental Implementation: 40%

• Develop, implement, and validate research algorithms.
• Maintain reproducible code and documentation.
• Conduct simulation experiments using driving simulators or other software tools to test and validate algorithms.
• Perform physical experiments on robotic and vehicle-based experimental platforms to evaluate real-world performance.
• Ensure safe operation, setup, and maintenance of research equipment and experimental platforms.

Communication and

Collaboration:

10%

• Prepare research reports, manuscripts, and presentations for conferences and publications.
• Collaborate with lab members, faculty, and external partners on research projects.
• Mentor graduate or undergraduate students as needed.

• Doctoral Degree in Automation, Robotics, Control, Computer Science, Electrical Engineering, Industrial Engineering, or a related field; advanced ABD will be considered and degree must be conferred by time of appointment.
• Strong background in one or more of the following areas:
  • Optimal control, MPC, RL, or reachability analysis.
  • Uncertainty quantification, stochastic modeling, or probabilistic reasoning.
• Proficiency in Python, C++, or MATLAB, with demonstrated ability to implement complex algorithms.
• Excellent written and oral communication skills.

• Experience with driving simulators (e.g., CARLA, SUMO) or robotic simulation environments (e.g., Isaac Sim, Gazebo).
• Experience…