Postdoc: Causal AI & Multimodal Health Data Science

Indiana University; Bloomington (IN); On-site; USD 55,000 - 75,000; Full time

Job summary

Indiana University in Indianapolis is seeking a Postdoctoral Fellow in Biostatistics and Health Data Science. The role focuses on advancing AI for healthcare, multimodal data analysis across EHRs, imaging, omics, and notes, with emphasis on causal AI and inference.

You will lead research projects, build reproducible pipelines (prefer Python), and apply causal methods to clinical questions, translating results into workflows while contributing to grants and reports.

Qualifications

• Experience with multimodal data (EHRs, medical imaging, omics, physiological signals, clinical notes).
• Strong background in causal inference and causal discovery methods.
• Proficiency in Python for research pipelines.

Responsibilities

• Lead original research in multimodal and causal AI for health; design, implement, and rigorously evaluate algorithms and full pipelines.
• Build reproducible research pipelines and maintain reliable experiment codebases.
• Apply causal inference and discovery frameworks to clinical questions.
• Translate proposed methods and frameworks into real-world clinical workflows.
• Contribute to grant proposals and research reports.

Skills

Multimodal data analysis

Causal AI

Python

Biostatistics

Tools

Git

Job description

Indiana University in Indianapolis is seeking a Postdoctoral Fellow in Biostatistics and Health Data Science. The role focuses on advancing AI for healthcare, multimodal data analysis across EHRs, imaging, omics, and notes, with emphasis on causal AI and inference.

You will lead research projects, build reproducible pipelines (prefer Python), and apply causal methods to clinical questions, translating results into workflows while contributing to grants and reports.