美国哈佛大学招聘博士后—差分隐私学习和可复现性

美国哈佛大学招聘博士后—差分隐私学习和可复现性

Postdoctoral Fellowship in Differentially Private Learning and Replicability

Harvard University

in Cambridge, MA

Type: Full-Time

Salary: $67,600 - $91,826 per year

Posted: Today

Category: Computer Science

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

Department/Area: Computer Science

Position Description

Cynthia Dwork, Gordon McKay Professor of Computer Science in Harvard SEAS' Theory of Computation Group, seeks applicants for a postdoctoral fellowship to conduct research in differentially private learning, its connections to replicability of algorithms, and algorithmic fairness.

Basic Qualifications

Candidates are required to have a doctorate or terminal degree in Computer Science or a related area by the expected start date.

Additional Qualifications

A demonstrated interest in algorithmic fairness is desirable, as well as a track record of publications in high-quality venues in the stated areas.

Special Instructions

Required application documents include a cover letter, CV, a statement of research interests, and up to three representative papers. Candidates are also required to submit the names and contact information for at least two (or up to three) references.

We encourage candidates to apply by March 2, 2026, but will continue to review applications for up to 30 days from date of posting.

Contact Information

Allison Choat (Faculty Coordinator, Theory of Computation Group)

Contact Email: achoat@seas.harvard.edu

Salary Range

$67,600 - $91,826

Pay offered to the selected candidate is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field.

Minimum Number of References Required: 3

Maximum Number of References Allowed: 5

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, or any other characteristic protected by law.

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