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Postdoctoral Fellow AI/ML for applications of AI to Population Health

A one-year postdoctoral position (with a potential for a second year) is available for an exciting collaborative project between the University of Houston (UH) College of Medicine (, the UH Computational Biomedicine Lab (CBL), and UH Population Health.

The position entails developing innovative training material and methods for using the FAIR principles ( to make research data findable, accessible, interoperative, and reusable (FAIR).  Such work can facilitate biomedical advancements by bolstering data labeling and management practices to enable artificial intelligence and machine learning innovations.

FAIR-Principles for Population Health Applications. The candidates will be part of a vibrant and pioneering team (scientists, clinicians, postdocs, graduate students). They will have the opportunity to develop training materials for making the data FAIR. Highly motivated and creative individuals with a recent Ph.D. in a computational discipline or population health-related field (with a strong quantitative emphasis) are encouraged to apply. The applicant needs to be goal-oriented, self-sufficient, and work independently and in groups in fast-paced research environment to succeed in this position. 

What we are looking for:

  • Talented and creative candidates who enjoy working in a multidisciplinary research environment
  • Scholars who have published (or aspire to publish) in top peer-reviewed journal
  • Strong AI background
  • Fluent communication skills (written and oral) in English

Application process:
The salary compensation is very competitive. For more information, please email Professor Kakadiaris ( For consideration, please submit your application preferably in one single PDF document including a cover letter, a full CV, a statement of research interests and career goals, and the names and email addresses of three references to, with the subject line "PH Application: (your name)." Application reviews will be conducted as the applications arrive. Final selections will be based on a Zoom interview with a panel of researchers.