Postdoctoral Fellowship in Electrical and Computer Engineering

School School: Harvard John A. Paulson School of Engineering and Applied Sciences 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. The 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. Contact Information Sarah Gayer Contact Email View email address on click.appcast.io 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. 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. #J-18808-Ljbffr Harvard University