Postdoctoral Appointee - AI for Biomedical Discovery
Core
Develop secure, scalable, and continuously learning AI systems for biomedical discovery using federated learning and foundation models.
Role type
Postdoctoral researcher (AI/ML)
Builds
Multimodal federated learning frameworks and agentic AI systems for biomedical applications
Domain
Biomedical AI, Federated Learning, Privacy-Preserving ML
Deliverable
production ML models
Required skills
Python, PyTorch/TensorFlow/JAX, federated learning, deep learning, multimodal data integration, continual learning, experimental design, scientific communication
Preferred skills
APPFL/Flower/FedML/NVIDIA FLARE, multimodal biomedical data (imaging/omics), foundation models/LLMs, agentic AI frameworks, differential privacy, Kubernetes/Docker, MLOps
Technologies
Python, PyTorch, TensorFlow, JAX, Kubernetes, Docker, Apptainer, Singularity
Responsibilities
Design and implement multimodal federated learning methods; develop continuous learning approaches for model improvement; build software capabilities in federated learning frameworks; evaluate model performance, robustness, and privacy; collaborate with interdisciplinary teams; prepare research for publication and present findings.
Seniority
Postdoctoral (0-5 years post-PhD)
