Argonne

Argonne

Postdoctoral Appointee - AI for Biomedical Discovery

Lemont, IL USA · Contract

Sponsorship not specified$73k-$121kDetected 49 days ago
PythonDockerKubernetesCI/CDMachine LearningDeep LearningTensorFlowPyTorchData ScienceLLMsAgentic AIMLOpsAI OrchestrationA/B TestingElectrical EngineeringBioinformaticsRadiologyResearchLeadershipCommunicationPublic Speaking

About the role

  • 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.
  • 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.
  • 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.

Responsibilities

  • 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.
  • Support project milestones, demonstrations, and deliverables by developing working prototypes, experimental benchmarks, and reusable software components.

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.
  • 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 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.
  • 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 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.

Nice to have

  • Preferred Skills and Qualifications:
  • Preferred Skills and

Compensation

  • Please note that the pay range information is a general guideline only.

Benefits

  • 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.
  • Build and extend software capabilities in federated learning frameworks, with emphasis on scalable, reproducible, secure, and extensible research software.
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