2027 Hertz Fellowship application info sessions offered for prospective applicants
Meeting Announcement
Updates every hour. Last Updated: 20-Oct-2026 07:16 ET (20-Oct-2026 11:16 GMT/UTC)
The Hertz Foundation will hold two Hertz Fellowship Application Information Sessions for applicants to learn more about the 2027 Hertz Fellowship application and experience as a fellowship recipient. On Thursday, September 10, at 6:00 p.m EDT -or- Monday, September 14 at 12:00 p.m. EDT, join Hertz Fellowship Director Derek Haseltine and Hertz Fellows for an online information session to learn more about this unique doctoral funding opportunity.
The Hertz Foundation today opened applications for the 2027 Hertz Fellowship, the nation’s most competitive doctoral fellowship in the applied sciences, engineering and mathematics. The Hertz Fellowship application closes on October 30, 2026. Fellowship finalists will be named in February 2027, and fellowship awards will be officially announced in May 2027.
This September 13-18, at the 13th Heidelberg Laureate Forum (HLF), 200 young researchers in mathematics and computer science will spend a week of scientific exchange with the laureates of the disciplines’ most prestigious prizes: the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing, Fields Medal, the Nevanlinna Prize, as well as its continuation, the IMU Abacus Medal. Six of the nine recipients of the fields’ most prestigious prizes awarded in 2026 will be among the 21 laureates taking part. Below are a few highlights of the upcoming program of the HLF as well as a breakdown of how to cover this unique event.
By combining two complementary clues—camera geometry and visual appearance—researchers at Institute of Science Tokyo, Japan, developed a new approach for preserving identities across multiple cameras. The method uses epipolar geometry to identify spatially consistent candidate matches and appearance similarity to distinguish between possible identities. The approach can be integrated with existing single-camera tracking systems without requiring environment-specific retraining.