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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Postdoctoral Fellow in Biostatistics & Health Data Science - **Company:** Indiana University Foundation - **Location:** Indianapolis, IN, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Health Informatics, Clinical Data Repository, Data Infrastructure, Data Integration, Python (Programming Language), Machine Learning, Open Source Technology, Pytorch, Fast Healthcare Interoperability Resources, Large Language Models, Git, Scikit Learn, Information Technology, Software Version Control - **Published:** September 3, 2026 - **Apply:** https://indiana.peopleadmin.com/postings/32238/pre_apply ## About the Role * 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., * 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 ## 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. This postdoctoral position addresses a fundamental and timely research question: 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., * 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 ## Related Videos - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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