Postdoctoral Appointee - AI Biomedical Discovery

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.

• 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, Tensor Flow, 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.

• 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,…