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, multimodal, biomedical, scientific, or high-dimensional data.
• Ability to work independently while contributing effectively to a multidisciplinary research team.
• Strong written and oral communication skills, including the ability to prepare manuscripts, technical reports, presentations, and documentation.
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.

Preferred Skills and Qualifications:

• Experience developing or extending federated learning frameworks such as APPFL, Flower, FedML, NVIDIA FLARE, or similar systems.
• Experience with multimodal biomedical data, including combinations of clinical records, medical imaging, pathology, genomics, transcriptomics, proteomics, wearable/sensor data, or scientific text.
• Familiarity with foundation models, large language models, vision-language models, biomedical AI models, or model fine-tuning methods such as LoRA, adapters, instruction tuning, or retrieval-augmented generation.
• Experience with continual learning, active learning, reinforcement learning, closed-loop learning, or human-in-the-loop AI workflows.
• Experience with agentic AI frameworks, tool-using LLMs, workflow orchestration, AI planning systems, or multi-agent systems.
• Familiarity with privacy and security techniques such as differential privacy, secure aggregation, secure multiparty computation, homomorphic encryption, trusted execution environments, or secure enclaves.
• Experience with distributed computing, cloud computing, containers, Kubernetes, Docker, Apptainer/Singularity, or high-performance computing environments.
• Experience with MLOps, reproducible workflows, experiment tracking, CI/CD, software testing, benchmarking, or open-source software development.
• Familiarity with biomedical AI validation, data readiness assessment, model evaluation, regulatory-grade evidence generation, or independent verification and validation workflows.
• Demonstrated ability to publish research, contribute to collaborative software projects, or present technical work to interdisciplinary audiences.

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.