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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

Keywords

Computer Science
Mechanical Engineering
Electrical Engineering
Control Theory
Safety
Robotics
Autonomous System
Autonomous Robotics
Machinelearning

Description

The SPARC (Safe + Performant Autonomous Robotics & Control) Lab at Tufts University, led by Assistant Professor Ryan K. Cosner, is recruiting talented, passionate, and driven engineers for a fully funded PhD position in Robotics, Control, and Machine Learning. The lab focuses on developing safe and performant robot autonomy at the intersection of robotics, control theory, and machine learning. Research in the lab aims to create deployable, trustworthy autonomy algorithms that enable robots to work confidently alongside humans, with a strong emphasis on application-motivated theory and real-world deployment. Potential research directions include aligning mathematical safety concepts with human preferences, developing methods to synthesize safety algorithms in novel environments, deriving safety guarantees using realistic uncertainty models, and studying the utility of combined first-principles and data-driven models to close the sim-to-real gap. The lab is equipped with state-of-the-art robotics hardware, including robots, sensors, computers, and simulation tools, to support experimental validation of theoretical work. Applicants should have a background in Mechanical Engineering, Electrical Engineering, Computer Science, or a closely related field, with demonstrated interest and experience in robotics, machine learning, and/or control theory. Proficiency in programming (C++, Python, Matlab, Julia), experience with robotics hardware, and strong communication and teamwork skills are required. Preferred qualifications include experience with machine learning frameworks (PyTorch, TensorFlow), probabilistic robotics, hardware design, sensing and perception, embedded devices, and relevant software tools (ROS/ROS2, Linux, CVX, Git, Solidworks, Latex). The position is fully funded for five years through Research or Teaching Assistantships, covering stipend and tuition. The start date is September 2026, with applications due by December 15, 2025. The lab encourages applicants from diverse backgrounds and offers a collaborative, supportive research environment. For more information, visit the SPARC Lab website or contact Professor Cosner directly. To apply, submit your application to the Tufts University Department of Mechanical Engineering PhD program, mention Professor Cosner in your application, and email him after submission. If the application fee is a hardship, contact the Tufts Office of Graduate Admissions.

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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