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Postdoctoral Fellow in Biostatistics and Health Data Science: Multimodal and Causal AI for Healthcare Indiana University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Country
United States
University
Indiana University
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About this position
Indiana University School of Medicine is seeking a Postdoctoral Fellow in Biostatistics and Health Data Science to advance research at the intersection of artificial intelligence for healthcare, multimodal data analysis, and causal AI.
The fellow will lead original research involving diverse healthcare data, including electronic health records, medical imaging, omics, physiological signals, and clinical notes. The work will focus on developing, implementing, and rigorously evaluating algorithms and complete research pipelines for multimodal learning, time-series modeling, natural language processing, causal inference, causal discovery, and counterfactual reasoning.
The position offers an opportunity to develop deployable and trustworthy methods that can improve patient outcomes and health-system operations. Research will also involve translating computational methods into real-world clinical workflows, building reproducible codebases, and contributing to grant proposals and research reports.
The fellow will join the Biostatistics and Health Data Science Department at the Indiana University School of Medicine’s Indianapolis campus. The department includes faculty and professional staff working across biostatistical, health data science, biomedical informatics, and related research areas. The research environment is highly interdisciplinary, with collaboration among computer scientists, biomedical informaticians, clinicians, and public health researchers.
Collaborative partners include IU Health, Eskenazi Hospitals, the Regenstrief Institute, the Center for Computational Biology and Bioinformatics, the Indiana Clinical and Translational Science Institute, the Indiana University Simon Comprehensive Cancer Center, the Indiana Institute of Biomedical Research, and the Indiana Alzheimer’s Disease Research Center.
The department’s research setting combines methodological development with clinical and health-system applications, providing opportunities to work on responsible artificial intelligence, multimodal healthcare analytics, and causal methods in partnership with major medical and research institutions.
Funding details
Available
What's required
Applicants must have a Ph.D. by the start date in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or a closely related area. Required qualifications include a strong foundation in machine learning and deep learning; expertise in at least one of multimodal learning, time-series modeling, or natural language processing; demonstrated experience with healthcare data such as electronic health records, clinical text, imaging, omics, or physiological signals; proficiency in Python and machine-learning tools such as PyTorch and scikit-learn; experience with Git and experiment tracking tools such as Weights & Biases; excellent written and oral communication skills; and the ability to collaborate with multidisciplinary teams. Preferred qualifications include experience with large language models or foundation models, multimodal transformers, causal methods or causal discovery, data engineering and MLOps tools, responsible and ethical AI for health, first-author publications in relevant venues, and collaborative open-source contributions. Required application documents are a curriculum vitae, letter of application, and list of references.
How to apply
Apply through the Indiana University PeopleAdmin posting at https://indiana.peopleadmin.com/postings/34978. Submit a curriculum vitae, letter of application, and list of references. The search continues until the positions are filled.
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.