The Johns Hopkins University Data Science and AI (DSAI) Institute welcomes applications for its postdoctoral fellowship program, seeking disciplinarily diverse scholars to advance foundational methods of data science and artificial intelligence, and their applications across all areas, including science, health, medicine, the humanities, engineering, policy, governance, democracy, and ethics.
The postdoctoral fellows will join the Institute's growing research community and collaborate with its broad array of faculty members. Fellows will form an interdisciplinary 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. At present, DSAI faculty represent multiple divisions and departments from across the university.
Fellows will collaborate with Institute members across the university, including the Homewood campus, the East Baltimore Health and Medicine campus, the JHU Applied Physics Laboratory in Laurel, MD, and the Washington D.C. campus.
Program Information and Benefits:
Positions are for 2 years with the possibility of an extension pending satisfactory performance and continued funding. While candidates who complete their applications by January 26, 2026 will receive full consideration, we may consider applications submitted after that date until all positions have been filled.
For questions or to request more information, please email ai@jhu.edu.
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.
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