Job Information
Harvard University Postdoc Fellow in Learning, Optimization, Control, and/or Robotics in Cambridge, Massachusetts
Details
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 achoat@g.harvard.edu
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
Keywords
Supplemental Questions