Postdoc: Causal Multimodal Models for Treatment Response

Purdue University; West Lafayette (IN); On-site; USD 52,000 - 78,000; Full time

Job summary

Purdue University invites applications for a Postdoctoral Research Associate position focused on causal foundation models for multimodal precision treatment-response discovery. The project integrates EHR trajectories, molecular profiles, and wearable sensor data to estimate treatment effects and heterogeneity in responses.

The fellow will combine representation learning, causal inference, and longitudinal modeling, guided by cross-disciplinary mentors.

Qualifications

• PhD in relevant field; completion by start date.
• Strong quantitative and computational skills in causal inference or ML.
• Experience with modern ML frameworks and scientific communication.

Responsibilities

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

Skills

Causal inference

Machine learning

Multimodal learning

Longitudinal modeling

Optimization

Biomedical data analytics

Python

Education

PhD in operations research/industrial or biomedical engineering/biomedical informatics/computer science

Tools

Python

Job description

Purdue University invites applications for a Postdoctoral Research Associate position focused on causal foundation models for multimodal precision treatment-response discovery. The project integrates EHR trajectories, molecular profiles, and wearable sensor data to estimate treatment effects and heterogeneity in responses.

The fellow will combine representation learning, causal inference, and longitudinal modeling, guided by cross-disciplinary mentors.