Postdoctoral Fellow in Biostatistics & Health Data Science

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

Job summary

Indiana University in Indianapolis invites applicants for a Postdoctoral Fellow in Biostatistics & Health Data Science to advance methods for harmonizing real-world health data using LLMs and ontology alignment. You will develop multi-agent workflows, collaborate across IU, Regenstrief Institute, and national consortia, and publish methods and results.

This role offers opportunities to work with multi-source datasets, contribute to open-source tooling, and mentor through grant writing and

Qualifications

• Ph.D. (by start date) in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or related field.
• Strong ML/deep learning foundation with expertise in multimodal learning, time-series, or NLP.
• Experience with healthcare data (e.g., EHR, clinical text, imaging, omics).
• Proficiency in Python, ML tooling (PyTorch, scikit-learn), Git, and experiment tracking.
• Excellent written and oral communication and collaboration skills.

Responsibilities

• 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

Skills

ML fundamentals

Python programming

Communication skills

Collaboration

EHR/health data experience

Education

Ph.D. in Computer Science/Biomedical Informatics/Health Data Science or related

Tools

PyTorch

scikit-learn

Git

Weights & Biases

NLP

Job description

Title Postdoctoral Fellow in Biostatistics & Health Data Science Specific Title Appointment Type Postdoctoral Fellow Department IUSM - Biostatistics Campus IU School of Medicine Indianapolis Position Summary

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.

How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization?

We are particularly interested in:

• 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).

Our Team’s Approach

We are not a pure 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.

Responsibilities

• 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

What We Offer

• 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 contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.

Indianapolis is the capital and most populous city in the State of Indiana. It is growing economically thanks to a strong corporate base anchored by the life sciences. Indiana is home to one of the largest concentrations of health sciences companies in the nation. Indianapolis has a sophisticated blend of charm and culture with a wonderful balance of business and leisure. The growing residential base is supported by rich amenities and quality of life – the city possesses a variety of professional sports, arts venues and outdoor recreation areas. Residents of this dynamic city, and surrounding suburbs, enjoy leading educational systems and top-ranked universities, paired with a diverse population. Indianapolis International Airport is a top-ranked international airport, being named “Best Airport in North America” by Airports Council International for many years. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocation.

The search will continue until the positions are filled.

Basic 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 (e.g., Weights & Biases).
• Excellent written and oral communication skills, and ability to collaborate with multidisciplinary teams.

Department Contact for Questions

Professor Jiang Bian via email at: [e-mail]

Additional Qualifications

Preferred Qualifications

• Experience with concept normalization, ontology mapping, or schema alignment
• Familiarity with LLM agents, tool-augmented reasoning, or hybrid rules + LLM systems
• Record of publications in relevant domains (informatics, machine learning, AI, knowledge representation)
• Experience with multi-site data harmonization or federated data environments

Special Instructions Priority Application Review Deadline Expected Start Date Posting Number IUSM-02358-2026