Postdoctoral Fellow in Optimization and Learning - Cambridge, United States - Harvard University

Harvard University Cambridge, United States

4 weeks ago

Description

Details

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.

Additional Qualifications

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

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

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