Postdoctoral Fellow in Biostatistics & Health Data Science

Research Context & Opportunity

Modern healthcare increasingly depends on integrating data across hospitals, registries, cohorts, and public health systems. Yet semantic heterogeneity—differences in terminology, structure, and logic—remains a central barrier to reusability, interoperability, and reproducibility.

This postdoctoral position addresses a fundamental and timely research question.

• LLM-driven systems for aligning real-world health data to standards like OMOP CDM, FHIR, and UMLS
• Agent-based workflows that explain, refine, and adapt semantic mappings over time
• Hybrid architectures that combine knowledge-grounded reasoning with flexible machine learning
• Tools that reduce manual burden while preserving traceability and clinical interpretability

This position offers the opportunity to publish novel methods, work with real messy multi-source data, and contribute to infrastructure supporting population-level research and health equity.

The postdoctoral fellow will be based in the Department of Biostatistics and Health Data Science at Indiana University School of Medicine
, in close collaboration with the Regenstrief Institute
, a nationally renowned center for health informatics research and real-world data infrastructure.

• Indiana University is home to one of the largest medical schools in the U.S., with extensive collaborations across informatics, clinical departments, and health systems (IU Health, Eskenazi Health, etc.).
• Regenstrief Institute is internationally recognized for its leadership in data standards (e.g., LOINC), clinical data networks (e.g., Indiana Network for Patient Care), and health information infrastructure.
• This environment supports both methodological research and operational implementation across state, national, and multi-institutional networks (e.g., NIH, OHDSI, PCORnet, ACT Network).

We are not a purely academic research group. We operate at the interface of research and health data operations
, building methods that not only publish but also deploy. We handle real clinical and public health data problems where ambiguity, variation, and scale are the norm—not the exception.

We welcome postdocs who want to drive innovation while engaging deeply with practical, meaningful data challenges.

• Design and implement LLM-based methods for clinical data harmonization, semantic normalization, and ontology alignment
• Develop multi-agent or RAG-style (retrieval-augmented generation) workflows for schema matching and terminology mapping
• Collaborate with national and multi-institutional initiatives in data integration and standardization
  
  Support open-source tooling, reproducible pipelines, and standards-based approaches (e.g., OMOP, FHIR, UMLS)
• Lead or support manuscript preparation and dissemination at top informatics and AI venues
• Contribute to grant development and proposal writing

• A collaborative environment at the intersection of real-world data, applied AI, and translational science
• Opportunities to work across academic, clinical, and public health settings
• Mentorship and support toward independent research or career development in academia or industry
• Competitive salary and benefits through Indiana University
• A culture that values both scientific innovation and practical impact

The Indianapolis Campus is the focal point of health professions education at Indiana University, and the School of Medicine is the country’s second largest allopathic medical school. Indianapolis consistently ranks high nationally on many of the “best places to live” lists and has an economy that is growing in the life sciences arena. In addition, it has always been one of the cities with the lowest cost of living.

Carmel, Indy’s northern neighbor, was recently named as the best mid-sized city in the country.

IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts…