Postdoctoral Fellow in Biostatistics & Health Data Science

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

Requirements

• 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.

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