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Harvard University Postdoc Fellow in Learning, Optimization, Control, and/or Robotics in Cambridge, Massachusetts


Title Postdoc Fellow in Learning, Optimization, Control, and/or Robotics

School Harvard John A. Paulson School of Engineering and Applied Sciences

Department/Area Electrical Engineering/ Applied Mathematics/ Computer Science

Position Description

Professor Na Li’s group and Professor Heng Yang’s group in the School of Engineering and Applied Sciences ( SEAS ) at Harvard University seek motivated postdoctoral fellows with a Ph.D. in electrical engineering, computer science, applied mathematics, or any related field.

Candidates who have a strong mathematical background in machine learning, reinforcement learning, optimization, control, computer vision, robotics are preferred. Candidates will perform research on learning, optimization, estimation, and control of robotics and autonomous systems with a focus on theory development, algorithm design, performance analysis, and real-world deployment. Candidates have the opportunity to work closely with either PI Li, or PI Yang, or both PIs, as well as the graduate students and collaborators.

Basic Qualifications

Ph.D. in electrical engineering, applied mathematics, computer science, or any related field.

Additional Qualifications

A good publication record, in addition to good teamwork and communication skills.

Special Instructions

A complete application must include a curriculum vitae, a research statement, 2-3 letters of reference, and three publication samples.

SEAS is dedicated to building a diverse and welcoming community, and we strongly encourage applications from historically underrepresented groups.

Contact Information

Allison Choat, Faculty Coordinator

Contact Email

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required 3

Maximum Number of References Allowed


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