Postdoctoral Fellowship

Position Description

Overview

The Johns Hopkins University Data Science and Artificial Intelligence (DSAI) Institute welcomes applications for its DSAI postdoctoral fellowship program, seeking scholars to advance DSAI and/or its applications across all areas, including science, health, medicine, the humanities, engineering, policy or ethics.

The Postdoctoral Fellows will join DSAI’s growing research community and collaborate with its broad array of faculty members. Fellows will form a cohort of outstanding scholars who will contribute to the research, education, policy, and outreach goals of the Institute.

The fellows program supports the historic expansion of the Whiting School of Engineering, which will add 150 new tenure-track professors at all ranks, including 30 Bloomberg Distinguished Professorships and 80 positions in DSAI. This expansion includes a new building, 30 research engineers, and extensive computational resources that will establish Johns Hopkins as one of the largest and leading engineering schools with a top AI research program. 

Program Information and Benefits

• Competitive salary

• Academic travel stipend

• Mentoring by DSAI-affiliated faculty and collaborators

• Access to DSAI compute resources

• Participation in center-supported educational initiatives and projects

Positions are for 2 years with the possibility of an extension pending satisfactory performance and continued funding. 

Application Process

While candidates who complete their applications by January 6, 2025 will receive full consideration, we may consider applications submitted after that date until all positions have been filled.

Additional Information

https://engineering.jhu.edu/Datascience-AI/

ai.jhu.edu

Contact

For questions or to request more information, please email [email protected].

The referenced salary range is based on Johns Hopkins University's good faith belief at the time of posting. The actual compensation offered to the selected candidate may vary and will be based on factors including, but not limited to, the experience and qualifications of the selected candidate – e.g., years in rank, training, field, discipline, other work experience, and other similar factors; geographic location; internal equity; external market conditions; and other factors as reasonably determined by the University.