Postdoctoral Fellow in Biostatistics and Health Data Science

Postdoctoral Fellow in Biostatistics and Health Data Science

Postdoctoral Researcher to advance research at the intersection of artificial intelligence for healthcare
, multimodal data analysis (EHRs, medical imaging, omics, physiological signals, clinical notes), and causal AI (causal inference, discovery, counterfactual reasoning). The successful candidate will collaborate with an interdisciplinary team of computer scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations.

• Lead original research in multimodal and causal AI for health; design, implement, and rigorously evaluate algorithms and full pipelines.
• Build reproducible research pipelines and maintain reliable experiment codebases (prefer Python).
• Apply causal inference and discovery frameworks to clinical questions.
• Translate proposed methods and frameworks into real-world clinical workflows.
• Contribute to grant proposals and research reports.

• 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 LLMs/foundation models (e.g., clinical NLP, retrieval-augmented generation, instruction tuning) and multimodal transformers.
• Solid understanding of causal methods (e.g., propensity scores, IPW, matching) and/or causal discovery.
• Familiarity with data engineering and MLOps (e.g., SQL, Spark, Airflow, Docker, Kubernetes).
• Knowledge of responsible/ethical AI for health: fairness/equity, interpretability, robustness, privacy (e.g., differential privacy, federated learning).
• Track record of first-author publications in relevant venues and collaborative open-source contributions.

Professor Jiang Bian via email at: bianj

• Full-time

• Internship

The search will continue until the positions are filled.