Postdoctoral Fellowship in Electrical and Computer Engineering

Harvard University; Cambridge (MA); Presencial; USD 67.600 - 91.826; Jornada completa

Descripción de la vacante

Harvard University invites applications for a Postdoctoral Fellowship in Electrical and Computer Engineering. The successful candidate will work in the Harvard Information Theory Laboratory, focusing on information-theoretic methods for AI. A PhD in a related field is required, and experience with information theory and machine learning is preferred. The position offers a competitive salary range of $67,600 to $91,826 and is funded for two years, beginning September 2026. Interested candidates should provide a CV, a Research Statement, and letters of recommendation.

Formación

• PhD must be completed by the start of the position.
• Experience in information theory and machine learning is preferred.

Responsabilidades

• Develop information-theoretic methods for AI systems.
• Work under supervision of Prof. Flavio Calmon.

Conocimientos

Publications in information theory

Proficiency in Python

Experience with GPU cluster environments

Educación

PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related discipline

Descripción del empleo

Postdoctoral Fellowship in Electrical and Computer Engineering

Title: Postdoctoral Fellowship in Electrical and Computer Engineering

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

Department/Area: Computer Science

Position Description

The John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Department of Statistics at Harvard University seek applicants for a postdoctoral fellow in Electrical and Computer Engineering.

This position is for a Postdoctoral Scholar in the area of information theory and artificial intelligence at the Harvard Information Theory Laboratory. The successful candidate will work under the supervision of Prof. Flavio Calmon at Harvard SEAS. The postdoctoral researcher will develop information-theoretic methods for alignment, privacy, and reliability in modern AI systems.

Basic Qualifications

Applicants must have a PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related discipline, or be confident of its completion by the start of this position.

Additional Qualifications

Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine learning and information theory, and expertise in at least one of the following areas is preferred: AI alignment, (differential) privacy, and coding or information theory for AI systems. Proficiency in Python and experience with GPU cluster environments (e.g., SLURM) are a plus.

Special Instructions

Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start date is September 2026 (flexible). The position is funded for two years.

Contact Information

Contact Email: [e-mail]

Salary Range

$67,600 – $91,826 (pay is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field).

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.
Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy. Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.