Postdoc: AI for Health Data Science & Causal Inference

Indiana University; Indiana; On-site; USD 70,000 - 100,000; Full time

Job summary

Indiana University invites applications for a Postdoctoral Fellow in Biostatistics and Health Data Science at the IU School of Medicine, Indianapolis.

The role focuses on advancing research at the intersection of artificial intelligence for healthcare, multimodal data analysis (EHRs, imaging, omics), and causal AI, collaborating with computer scientists, clinicians, and public health researchers to create deployable, trustworthy methods.

Qualifications

• Ph.D. (by start date) in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or a closely related area.
• Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP.
• Demonstrated working experience with healthcare data (e.g., EHR, clinical text, imaging, omics).
• Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (Weights & Biases).
• Excellent written and oral communication skills, and ability to collaborate with multidisciplinary teams.

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 (prefer Python).
• 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

ML foundations

Multimodal learning

Time-series modeling

NLP

Python

PyTorch

Git

Experiment tracking

Education

PhD in Computer Science/Biostatistics/Health Data Science

Tools

PyTorch

scikit-learn

Weights & Biases

Git

Job description

Indiana University invites applications for a Postdoctoral Fellow in Biostatistics and Health Data Science at the IU School of Medicine, Indianapolis.

The role focuses on advancing research at the intersection of artificial intelligence for healthcare, multimodal data analysis (EHRs, imaging, omics), and causal AI, collaborating with computer scientists, clinicians, and public health researchers to create deployable, trustworthy methods.