**Job Description**
The Post Doctoral Associate performs scientific research and studies under supervision, contributing to the scientific community. This role focuses on launching new interdisciplinary robotics research and educational initiatives within the ECE Department and across the College of Engineering. Key responsibilities include leading research in AI-powered and AI-enabled robotic systems such as deep reinforcement learning, computer vision, and human-robot interaction, and facilitating collaborations with the UM Miller School of Medicine, particularly in medical robotics for surgical and assistive applications. The associate will also assist with setting up a new research lab, support grant proposal submissions, mentor students, and present research at conferences.
**Skills & Abilities**
• Learning Agility: Ability to learn new procedures, technologies, and protocols, and adapt to changing priorities and work demands.
• Teamwork: Ability to work collaboratively with others and contribute to a team environment.
• Technical Proficiency: Skilled in using office software, technology, and relevant computer applications.
• Communication: Strong and clear written and verbal communication skills for interacting with colleagues and stakeholders.
• Strong programming skills (Python, C++, MATLAB).
• Experience with software tools such as TensorFlow, ROS, Gazebo, and OpenCV.
• Familiarity with robotic hardware platforms (e.g., Kinova robotic arms, UAVs, mobile robots) and real-time operating systems.
• Excellent communication skills and ability to work in cross-functional and interdisciplinary teams.
• Demonstrated initiative, creativity, and problem-solving ability in research and development environments.
**Qualifications**
Required Degree(s) in:
• Electrical and Computer Engineering
• Robotics
• Mechanical Engineering
• Closely related Engineering field
**Experience**
Other:
• Extensive experience in robotics system design, real-time control, and embedded systems integration.
• Demonstrated expertise in artificial intelligence, including reinforcement learning, computer vision, and deep neural networks applied to robotics.
• Track record of peer-reviewed publications and active participation in interdisciplinary research projects.
• Experience with grant writing, mentoring students, and collaborative academic or industry research is preferred.
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