Apply now: Emerging Leaders in Transportation 2017
The Emerging Leaders in Transportation fellowship program aims to enhance the toolkit of early-career employees to make transportation more efficient, effective and people-oriented.
In this competitive fellowship program, participants will learn from top transportation and management professionals to enhance leadership skills, communication techniques and policy work to bring innovative ideas into practice.
The 2017 program will take place on December 7 and 8 at the NYU Rudin Center, 295 Lafayette Street, NY, NY. The agenda includes:
- Leadership sessions, where emerging leaders will collaborate on long-term leadership goals and developing innovative projects and ideas within an organization
- Behind-the-scenes visits to major transportation facilities for hands-on learning about industry goals and challenges
- A networking reception with Emerging Leaders alumni
Discussion topics will include: leadership, innovation, communications, building support for innovation, and practical applications. Sessions will include talks from and with esteemed professionals and group discussions and exercises. Participants will develop plans to introduce innovative solutions or concepts within their workplaces.
View a recap of last year’s fellowship program here.
Apply by clicking here.
Application Timeline:
- July 26: Application period opens
- September 16: Applications due
- October 2: Fellowship class selection announcement
- December 7-8: Fellowship program
Details:
- The Emerging Fellows program is open to transportation professionals with up to 10 years of experience.
- There is no cost for participating in the program.
- Applicants are welcome from any location; however, we are unable to subsidize travel or lodging for participants.
- No AICP or other continuing education credit is available for this program.
- Previous applicants are welcome to re-apply. Past participants are ineligible.
If you have questions about this program, please email rudin.center@nyu.edu.
This program is supported by a grant from the C2SMART University Research Center.