Postdoctoral Fellow in Optimization and Learning at Harvard University in Cambridge, Massachusetts

Job Description:

Harvard University


Title: Postdoctoral Fellow in Optimization and Learning

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

Department_Area: Electrical Engineering

Position Description: Professor Heng Yang's computational robotics 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 analytical background in machine learning foundations, computer vision and perception, statistical learning theory, reinforcement learning, control theory and decision science, and mathematical optimization 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 will also work closely with graduate students and collaborators.

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


Special Instructions: Application materials include an updated CV, a cover letter describing research interests and goals, a research statement, and up to three relevant scientific papers. Please submit the names and contact information for up to five references.

We strongly encourage applications from members of underrepresented groups.

Contact Information: Hayden Weaver

Contact Email: hweaver@seas.harvard.edu

Equal Opportunity Employer: Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.

Minimum Number of References Required: 3

Maximum Number of References Allowed: 5


Supplemental Questions: Required fields are indicated with an asterisk (*).

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