Postdoctoral Fellow in AI, Causal Inference, and Health Data Science
Suarez-Farinas Lab – Icahn School of Medicine at Mount Sinai

About the Institution
The Icahn School of Medicine at Mount Sinai is a globally recognized leader in medical education, scientific research, and innovative patient care. As the academic hub of the Mount Sinai Health System, it spans eight hospital campuses and includes a distinguished faculty of over 5,000 members. The institution is known for its pioneering spirit, investing in transformative technologies and fostering collaborative, multidisciplinary research to advance biomedical science and improve patient outcomes.

• PhD in statistics, biostatistics, computer science, data science, bioinformatics, or a related quantitative field
• Strong background in ML and interest or experience in causal inference (e.g., causal ML, treatment effect estimation)
• Proficiency in R and/or Python, with experience handling large, complex datasets
• Solid understanding of statistical modeling, experimental design, and algorithm development
• Experience in biomedical or healthcare research is a plus
• Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
• Strong written and oral communication skills
• Experience mentoring or teaching is desirable

Application
This is a full-time postdoctoral position. Applications will be reviewed on a rolling basis.

To apply, please email a CV and the names of three references to Mayte Suarez-Farinas(mayte.suarezfarinas@mssm.edu) with the subject line ?Postdoc Position.?

 

SPOC-UAW Local 4100 at Icahn School of Medicine (Post Docs), 811 - Population Health Science and Policy - ISM, Icahn School of Medicine

About the Lab and Role
The Suarez-Farinas Lab is seeking a highly motivated postdoctoral fellow to work at the intersection of artificial intelligence, causal inference, and translational health data science. Our research develops rigorous statistical and machine learning methods to uncover disease mechanisms, identify treatment-response biomarkers, and advance precision medicine using clinical trials, real-world data, and multi-omics datasets. A central focus of the lab is moving beyond purely predictive models toward causal, mechanistic, and clinically actionable insights, while building scalable and reproducible analytical pipelines.

The postdoctoral researcher will lead and contribute to cutting-edge projects involving AI and data science in healthcare. Responsibilities include designing studies, developing novel algorithms, analyzing large and complex datasets, and collaborating closely with clinicians and interdisciplinary teams. The fellow will contribute to high-impact publications, present at leading conferences, and mentor junior trainees.

This is a full-time, on-site position based in New York, United States.