Indiana University
Postdoctoral Fellow in Biostatistics and Health Data Science
Indiana University, Indianapolis, Indiana, us, 46262
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.
Key Responsibilities
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.
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.
Preferred Qualifications
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.
Department Contact Professor Jiang Bian via email at: bianj@regenstrief.org
Employment Type Full-time
Seniority Level Internship
The search will continue until the positions are filled.
#J-18808-Ljbffr
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.
Key Responsibilities
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.
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.
Preferred Qualifications
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.
Department Contact Professor Jiang Bian via email at: bianj@regenstrief.org
Employment Type Full-time
Seniority Level Internship
The search will continue until the positions are filled.
#J-18808-Ljbffr