A funded Ph.D. student opening is available for Spring 2020 in the Department of Computer Science at the University of Massachusetts Lowell.
Supervisor: Reza Ahmadzadeh, Ph.D.
Research topics include but not limited to: Robot Learning, Reinforcement Learning, Inverse Reinforcement Learning, Imitation Learning, Learning from Demonstration, and beyond.
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Mission of the lab
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The mission of our lab, PeARL (Persistent Autonomy and Robot Learning Lab) is to develop novel robot learning algorithms that will allow robotic platforms to interact with humans effortlessly, learn new skills autonomously, and generalize their capabilities robustly in dynamic environments. To achieve these goals, we investigate how machine learning, AI, and novel sensors/hardware can be used to advance the state of the art of robot learning and to improve robots' level of autonomy. Our research is carried out on manipulators, mobile robots, and mobile manipulation platforms. We are looking for motivated researchers to help make this vision a reality!
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Job Description
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You will work on novel robot learning algorithms that combine model-based and machine learning methods (both Imitation Learning and/or (Inverse) Reinforcement Learning) to accomplish effortless physical human-robot interaction or persistent autonomy in dynamic and harsh environments. The algorithms can be designed for fixed manipulator arms and/or mobile manipulator platforms, as well as walking robots.
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Benefits
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The assistantship includes a tuition waiver and a graduate student stipend. UMass Lowell is a Carnegie Doctoral High Research (RU/H) university ranked in the top tier of US News' National Universities and is located 30 miles northwest of Boston in the northeast Massachusetts high-tech region. There are over 120 robotics companies in the local area.
The successful candidate will have access to the PeARL lab (Persistent Autonomy and Robot Learning Lab) and several robotic platforms including manipulator, mobile, walking, and mobile manipulator robots.
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Your Skills
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-- A passion for robotics, modeling, mathematics, programming and abstract thinking
-- Excellent written and spoken English skills
-- A master's degree in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, or related fields.
-- A strong background in Linear Algebra, Calculus, Probability and Statistics, and Algorithms
-- (Preferable) Strong programming skills in Python, C++, and/or MATLAB
-- (Plus) Familiarity with tools such as ROS, Gazebo, MoveIt, YARP, Tensorflow, Pytorch, and OpenCV
-- We do care about grades and letters, at least to some extent. Same for TOEFL/GRE scores.
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Start Date
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The evaluation of the received applications starts immediately and will continue until the positions are filled. Interested students are strongly encouraged to apply early, as the hire of successful candidates will take place on the first-come-first-served basis. The desired start date is January 2020.
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To Apply
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Please send, as a single PDF document:
- A detailed Curriculum Vitae (please include: your nationality for visa requirements, date of birth, your English level, scientific publications if any, programming skills, and hobbies)
- A copy of academic records/transcript and most recent diploma
- A copy of applicant's relevant publications (if any)
- A one-page research proposal that can potentially lead to a major (theoretical) contribution. An option is to look at my papers, pick a topic that interests you, and write about your ideas related to my work. Merely repeating what my work was about (or a few keywords) will not be enough.
- List of at least 2 referees (support-letter writers). Support letters will be requested only if your application is considered. So you don't need to upload them now.
- Your homepage and link to videos and code if available.
The application files should be sent as a single PDF file to: reza@cs.uml.edu
