Postdoctoral Appointee - AI for Biomedical Discovery

Argonne National Laboratory; Illinois; On-site; USD 65,000 - 85,000; Full time

Job summary

Argonne National Laboratory seeks two postdoctoral researchers to help shape secure, scalable, and continuously learning AI systems for biomedical discovery. You will contribute to the Forge project, developing multimodal AI capable of learning across distributed data environments without centralizing sensitive data.

You'll work at the intersection of federated learning, foundation models, privacy-preserving ML, and continual learning, collaborating with scientists, software engineers, and HPC

Qualifications

• PhD completed within the last 0-5 years in a relevant field as listed.
• Strong Python programming and software development experience.
• Experience with ML/DL frameworks and federated learning concepts.

Responsibilities

• Conduct research in federated learning and multimodal AI for biomedical data.
• Develop methods for continual learning and adaptive federated training.
• Collaborate with computer scientists, domain scientists, and HPC experts; publish results.

Skills

Python programming

Experiment design

Communication of results

Interdisciplinary collaboration

Education

PhD in Computer Science / Data Science / Biomedical Informatics / Computational Biology / related field

Tools

PyTorch

TensorFlow

JAX

Job description

The Argonne team is seeking two highly motivated postdoctoral researchers to help shape the next generation of secure, scalable, and continuously learning AI systems for biomedical discovery. This position will contribute to the Forge project, which is focused on developing advanced multimodal AI capabilities that can learn across distributed data environments without requiring sensitive data to be centralized.

The successful candidates will work at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continuous learning, and agentic AI systems. This is an opportunity to conduct applied research that advances trustworthy AI for biomedical and national security-relevant use cases while working in a multidisciplinary environment that brings together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts.

You will help design and implement new methods for multimodal federated learning across heterogeneous data types such as clinical, imaging, omics, text, and experimental data. The work will include developing approaches for continual model improvement, adaptive federated training, model evaluation, workflow automation, and AI-assisted orchestration of distributed learning tasks. The position will also provide opportunities to contribute to open-source software, publish research findings, present at major conferences and workshops, and collaborate with partners across national laboratories, universities, government agencies, and biomedical research organizations.

The work will take place in a collaborative, mission-driven research environment that values technical creativity, rigorous engineering, scientific impact, and teamwork. The group works on practical AI systems that connect research prototypes to real-world deployment environments, including cloud, secure enclaves, trusted research environments, and leadership computing platforms. Candidates should be comfortable working in a fast-moving research setting where methods development, software implementation, experimentation, and scientific communication are all important parts of the role.

Core Responsibilities

• Conduct research and development in federated learning, privacy-preserving machine learning, multimodal AI, and foundation model adaptation for biomedical and related scientific applications.
• Develop new methods for multimodal federated learning that can integrate information across distributed datasets, including imaging, omics, clinical, text, sensor, and other structured or unstructured data modalities.
• Design and implement continuous learning approaches that allow models to improve over time as new data, validation results, or experimental feedback become available.
• Explore agentic AI approaches for federated learning, including AI agents that can assist with task orchestration, experiment planning, model evaluation, workflow automation, and decision support across distributed environments.
• Build and extend software capabilities in federated learning frameworks, with emphasis on scalable, reproducible, secure, and extensible research software.
• Evaluate model performance, robustness, generalizability, fairness, privacy, and data readiness across heterogeneous sites and datasets.
• Contribute to the design of secure AI workflows that may involve trusted research environments, secure enclaves, privacy-preserving computation, differential privacy, secure aggregation, or related techniques.
• Collaborate with interdisciplinary teams, including AI researchers, biomedical scientists, software engineers, security experts, and high-performance computing specialists.
• Prepare research results for publication in peer-reviewed conferences and journals, and communicate findings through presentations, technical reports, project meetings, and software documentation.
• Support project milestones, demonstrations, and deliverables by developing working prototypes, experimental benchmarks, and reusable software components.

Position Requirements

Required Skills and Qualifications

• Ph.D. completed within the last 0-5 years in computer science, data science, biomedical informatics, computational biology, bioengineering, applied mathematics, electrical engineering, or a related field.
• Strong programming skills in Python and experience developing research or production-quality machine learning software.
• Experience with machine learning or deep learning frameworks such as PyTorch, TensorFlow, JAX, or similar tools.
• Knowledge of federated learning, distributed machine learning, privacy-preserving AI, foundation models, multimodal learning, continual learning, or related areas.
• Ability to design and conduct computational experiments, analyze model performance, and communicate results clearly.
• Experience working with large-scale or complex datasets, including structured, unstructured, multimoda