An Electrical Engineer designs, develops, and tests products and systems that use or produce electricity, including power generators, electric motors, radar and navigation systems, and communication systems. Works on larger electrical systems rather than small electronic products or devices.
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 seeks 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 Sarah Gayer Contact Email:
sgayer@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
