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Ryan K. Cosner
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PhD Position in Robotics, Control, and Machine Learning at Tufts University SPARC Lab Tufts University in United States
I am recruiting a fully funded PhD student in robotics, control, and machine learning at Tufts University.
Tufts University
United States
email-of-the@publisher.com
Dec 15, 2025
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Description
Funding
Funding and benefits will be provided through Research Assistantships (RA) or Teaching Assistantships (TA) in accordance with the Tufts Graduate Assistants Collective Bargaining Agreement. The position is fully funded for five years, including stipend and tuition coverage.
How to apply
Apply directly to the Tufts University Department of Mechanical Engineering Ph.D. Program. Program details and application instructions are available on the Tufts ME PhD webpage. Mention Professor Cosner's name in your application and email ryan.cosner@tufts.edu after submitting your application. Contact the Tufts Office of Graduate Admissions if the application fee is a hardship.
Requirements
Applicants must have a Bachelor's or Master's degree in Mechanical Engineering, Electrical Engineering, Computer Science, or a closely related field by Fall 2026. Required qualifications include demonstrated interest in robotics, machine learning, and/or control theory; coursework in dynamics and control theory; proficiency in a programming language such as C++, Python, Matlab, or Julia; demonstrated experience with robotics hardware; academic communication experience (a research publication is not required, but applicants must be comfortable communicating technical ideas in a research context); and teamwork, independent research, and project management skills. Preferred qualifications include experience using machine learning tools (e.g., PyTorch, TensorFlow), coursework in probability, data science, probabilistic robotics, linear algebra, analysis, optimization, and nonlinear control, record of academic research communication, robotics hardware design and integration experience, experience with robot sensing and perception methods, experience with edge and embedded compute devices (e.g., NVIDIA Jetson, RaspberryPi, Teensy, Arduino), experience with robot simulators (e.g., NVIDIA Isaac Sim, MuJoCo), and experience with relevant software and computing tools such as ROS/ROS2, Linux, CVX, Git, Solidworks, and Latex.
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