Postdoc: Causal AI for Multimodal Health Insights

American Medical Informatics Association, Inc; Indiana; On-site; USD 48,000 - 60,000; Full time

Job summary

Purdue University invites applications for a Postdoctoral Research Associate to develop a causal multimodal foundation model using EHR trajectories, molecular profiles, and wearable data. The fellow will work under co-mentors Nan Kong and Jingchuan Serena Guo, contributing to representation learning, causal inference, and longitudinal analysis.

The role emphasizes interdisciplinary collaboration, mentorship, and professional development to support an independent academic career.

Qualifications

• PhD completed or near completion by start date.
• Strong quantitative and computational skills with experience in causal inference and ML.

Responsibilities

• Develop and evaluate new methods combining multimodal representation learning and causal treatment-effect estimation.
• Integrate and analyze biomedical data; assess transferability, uncertainty, and subgroup responses.
• Lead manuscripts, contribute to grant proposals, and collaborate across engineering, data science, and health sciences.

Skills

Causal inference

Multimodal learning

Longitudinal modeling

Optimization

Biomedical data analytics

Python

Machine learning frameworks

Education

PhD in operations research, industrial or biomedical engineering, biomedical/health informatics, computer science, statistics, biostatistics, computational biology, or related field

Tools

Python

PyTorch

TensorFlow

Job description

Purdue University invites applications for a Postdoctoral Research Associate to develop a causal multimodal foundation model using EHR trajectories, molecular profiles, and wearable data. The fellow will work under co-mentors Nan Kong and Jingchuan Serena Guo, contributing to representation learning, causal inference, and longitudinal analysis.

The role emphasizes interdisciplinary collaboration, mentorship, and professional development to support an independent academic career.