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Olemiss

Postdoctoral Research Associate - Robotics and AI

LocationUniversity, MS
Work modeon-site
Typefull-time
SeniorityEntry
DepartmentMechanical Engineering
Company size10,000+ people
PostedJul 27, 2026 · 2mo ago
Verified live14h ago
At a glanceSummarised by Seekless from the posting.
Ph.D. in Mechanical Engineering, Electrical or Computer Engineering, Computer Science, Robotics, Aerospace Engineering, Mechatronics, or a closely related field by the time of appointment
Research experience in one or more areas of robotics or autonomous systems
Hands-on experience building or deploying robotic systems on physical hardware (e.g. ROS / ROS 2), or a clear track record of moving work from simulation to hardware
Proficiency in Python and modern machine-learning tools (e.g. PyTorch), and systems-level programming (C++ / ROS)
A strong publication record relative to career stage, including at least one first-author paper, and excellent written and verbal communication in English
Interest or experience in making AI-enabled systems trustworthy: verification and validation, uncertainty quantification, runtime monitoring, safety cases, or reliability
Experience with medical or physiological devices, or with industrial automation and manufacturing robotics
Experience contributing to research proposals and mentoring students across disciplines
Skills
Python
PyTorch
C++
ROS / ROS 2
The University of Mississippi — fondly referred to as Ole Miss — stands as a premier public research institution with a proud legacy of academic distinction. We are devoted to nurturing a vibrant, inclusive community where every member — student, faculty, and staff — can achieve their fullest potential.
Job Summary:
The Department of Mechanical Engineering at the University of Mississippi seeks a Postdoctoral Research Associate in robotics and AI to join Dr. Xiaopeng Zhao’s research group. We welcome applicants from across engineering and computer science with expertise in any area of robotics, including manipulation, mobile and legged robots, autonomous navigation, perception and computer vision, motion planning and control, learning-based control, human-robot interaction, and sensing and state estimation.
The group’s distinctive focus is making AI-enabled robots reliable and deployable in the real world: not only building capable systems, but validating them, monitoring them, and establishing the credibility that lets them be trusted in clinical and industrial settings. You will apply your robotics expertise to real hardware on various testbeds. The role includes a named part in proposal development, mentoring of graduate students, and a clear path toward research-faculty status as external funding grows.
Job Description:
•
Develop, implement, and deploy robotics and AI methods on real hardware, in the area that fits your strengths (for example perception, control, planning, learning, manipulation, navigation, or human-robot interaction).
•
Help make those systems reliable and deployable: validation, testing, runtime monitoring, and building the case for safe real-world use.
•
Lead publications, present at leading venues, and co-develop federal and foundation proposals.
•
Mentor graduate and undergraduate students and develop reusable methods, demonstrations, and shared testbeds that build lasting research capacity.
•
Collaborate with clinical, industrial, and academic partners across disciplines.
Job Responsibilities:
Required Qualifications
•
Ph.D. in Mechanical Engineering, Electrical or Computer Engineering, Computer Science, Robotics, Aerospace Engineering, Mechatronics, or a closely related field by the time of appointment.
•
Research experience in one or more areas of robotics or autonomous systems (for example manipulation, mobile or legged robots, navigation, perception, motion planning and control, learning-based control, sensing and state estimation, or human-robot interaction).
•
Hands-on experience building or deploying robotic systems on physical hardware (for example ROS / ROS 2), or a clear track record of moving work from simulation to hardware.
•
Proficiency in Python and modern machine-learning tools (for example PyTorch), and systems-level programming (C++ / ROS).
•
A strong publication record relative to career stage, including at least one first-author paper, and excellent written and verbal communication in English.
Preferred Qualifications
•
Interest or experience in making AI-enabled systems trustworthy: verification and validation, uncertainty quantification, runtime monitoring, safety cases, or reliability.
•
Experience with medical or physiological devices, or with industrial automation and manufacturing robotics.
•
Experience contributing to research proposals and mentoring students across disciplines.
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