Postdoc: Federated Multimodal AI for Biomedical Discovery

Argonne National Laboratory; United States; On-site; USD 72,879 - 121,465; Full time

Job summary

Argonne National Laboratory seeks two postdoctoral researchers to shape the next generation of secure, scalable AI systems for biomedical discovery. This role contributes to the Forge project, building advanced multimodal AI that learns across distributed data without centralizing sensitive information.

You will work at the intersection of federated learning, foundation models, multimodal biomedical AI, and privacy-preserving machine learning, collaborating with computer scientists, domain

Qualifications

• Ph.D. completed within the last 0–5 years in computer science, data science, biomedical informatics, computational biology, bioengineering, or related fields.
• Strong programming skills in Python and experience developing research or production-quality ML software.
• Experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Knowledge of federated learning, distributed ML, privacy-preserving AI, foundation models, multimodal learning, or continual learning.

Responsibilities

• Conduct research and development in federated learning, privacy-preserving ML, multimodal AI, and foundation model adaptation for biomedical and related scientific applications.
• Develop new methods for multimodal federated learning across distributed datasets including imaging, omics, clinical, text, and sensor data.
• Design and implement continual learning approaches and AI-assisted orchestration of distributed learning tasks.

Skills

Python

PyTorch

TensorFlow

JAX

Federated learning

Privacy-preserving ML

Multimodal AI

Research communication

Independent work

Education

PhD in a related field

Job description

Argonne National Laboratory seeks two postdoctoral researchers to shape the next generation of secure, scalable AI systems for biomedical discovery. This role contributes to the Forge project, building advanced multimodal AI that learns across distributed data without centralizing sensitive information.

You will work at the intersection of federated learning, foundation models, multimodal biomedical AI, and privacy-preserving machine learning, collaborating with computer scientists, domain