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Argonne National Laboratory
2 weeks ago
Postdoctoral Appointee - AI for Biomedical Discovery Argonne National Laboratory in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full-time postdoctoral appointment at Argonne National Laboratory with an expected hiring range of $72,879.00-$121,465.00 plus comprehensive benefits.
Country
United States
University
Argonne National Laboratory
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About this position
Postdoctoral Appointee - AI for Biomedical Discovery at Argonne National Laboratory in Lemont, Illinois, USA.
This postdoctoral opening seeks two highly motivated researchers to help develop the next generation of secure, scalable, continuously learning AI systems for biomedical discovery. The project is part of the Forge effort and focuses on advanced multimodal AI that can learn across distributed data environments without centralizing sensitive data.
The research sits at the intersection of federated learning, foundation models, multimodal biomedical AI, privacy-preserving machine learning, continual learning, and agentic AI systems. Work includes developing methods for multimodal federated learning across heterogeneous data such as clinical, imaging, omics, text, and experimental data; building adaptive training and workflow automation; and contributing to secure research software and open-source tools.
Responsibilities include designing and implementing scalable federated learning methods, evaluating robustness/fairness/privacy, contributing to secure AI workflows, collaborating with interdisciplinary teams, and publishing results in conferences, journals, and technical reports.
Eligibility: 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 Python and machine learning software experience are required, along with familiarity in PyTorch/TensorFlow/JAX and knowledge of federated learning, distributed ML, privacy-preserving AI, foundation models, multimodal learning, or continual learning.
Preferred background: federated learning frameworks such as APPFL, Flower, FedML, or NVIDIA FLARE; multimodal biomedical data; LLMs and vision-language models; continual/active/reinforcement learning; agentic AI frameworks; privacy/security techniques such as differential privacy or secure aggregation; distributed computing, containers, Kubernetes/Docker, HPC, and MLOps.
Funding: Full-time postdoctoral role with an expected salary range of $72,879.00-$121,465.00 and comprehensive benefits.
Location: Lemont, IL USA.
How to apply: Apply via the Argonne Workday job page. The posting also notes background checks and possible government access authorization requirements. For support, contact [email protected].
Funding details
Full-time postdoctoral appointment at Argonne National Laboratory with an expected hiring range of $72,879.00-$121,465.00 plus comprehensive benefits.
What's required
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 Python programming and experience developing research or production-quality machine learning software are required. Candidates should know machine learning or deep learning frameworks such as PyTorch, TensorFlow, or JAX, and have knowledge of federated learning, distributed machine learning, privacy-preserving AI, foundation models, multimodal learning, continual learning, or related areas. Applicants should be able to design and conduct computational experiments, analyze model performance, work with large-scale or complex biomedical/scientific datasets, collaborate in multidisciplinary teams, and communicate clearly in writing and speech.
How to apply
Apply through the Argonne Careers Workday posting using the Apply link on the job page. Prepare a CV and application materials highlighting your PhD, Python/ML experience, federated learning or biomedical AI background, and relevant publications or software work. Be aware that background checks and potential government access authorization requirements apply.
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