Post Doctoral Research Associate x2013; Automation and Intelligent Systems

The University of Texas at Arlington Research Institute (UTARI), a university-level applied research organization operating out of the Office of Research and Innovation at the University of Texas at Arlington, invites applications for a Postdoctoral Research Associate, Non-Tenure status..The Postdoctoral Research Associate will work with the team in UTARIand#x2019;s Automation and Intelligent Systems Division. In this role, the Associate will assist with research and development projects in the areas of robotics, machine learning, control systems, and/or computer vision. They will be responsible for: designing and analyzing algorithm sensing, navigation and control of robot systems, including mobile robots and robot arms/manipulators; designing and implementing algorithms for machine learning, computer vision, and/or estimation and control; conducting experimental validation of the algorithms on hardware platforms; providing some supervision over students; assist in identifying relevant funding opportunities; contributing to writing efforts on proposals, publications, reports, and presentations; and developing academic and industry collaboration partnerships; and maintaining awareness of current technological advancements related to your field..PhD in Electrical Engineering, Mechanical Engineering, Computer Engineering, Computer Science, or other relevant engineering or science discipline.Expertise in the field of robotics and in at least one additional field of control systems, machine learning, and computer vision.Ability to multi-task, collaboratively work in interdisciplinary teams, and work on projects independently.Able to present findings/results through both oral and written communication to internal and external stakeholders.Strong research record evidenced in peer-reviewed publications..Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch, Tensorflow, scientific communication, and LaTeX.