Postdoctoral Opportunity in Multimodal Foundation Models & Clinical Translation at Mayo Clinic
The MI2 Lab at Mayo Clinic, Arizona, is offering a postdoctoral opportunity focused on advancing next-generation multimodal foundational models for clinical translation. The lab specializes in developing scalable AI architectures that integrate imaging, clinical text, and longitudinal EMR data to support precision diagnostics and real-world decision-making across diverse patient populations. This interdisciplinary team operates at the intersection of AI research, clinical practice, and large-scale healthcare data, providing a unique environment for postdocs aiming to make a direct impact on patient care.
Research Focus Areas:
Multimodal Foundational Models:
Pretraining large-scale models across imaging (CT, MRI, mammography), clinical narratives, and structured EMR; learning robust, generalizable representations for downstream clinical tasks; designing model architectures that unify pixel-level, text-level, and temporal signals.
Translational AI & Clinical Deployment:
Building end-to-end pipelines for clinical workflows; evaluating models for OOD detection, fairness, interpretability, and reliability; collaborating with radiologists and clinicians to co-design real-world tools.
Longitudinal Patient Modeling:
Predictive modeling over multi-year EMR trajectories; risk stratification for chronic and progressive diseases; early disease detection using multimodal temporal signatures.
Ideal Candidate:
Applicants should have a PhD in Computer Science and Engineering, a strong background in machine learning, and experience with NLP, medical imaging, or multimodal architectures is preferred. Interest in translational research and cross-disciplinary collaboration is important, along with strong English communication skills.
Funding & Benefits:
The position offers a competitive salary, comprehensive benefits, and VISA sponsorship for international candidates. Postdocs will have access to Mayo Clinic’s computational resources, imaging archives, EMR datasets, and clinical collaborators, with opportunities to publish in top-tier venues and contribute to real-world clinical tools. Immediate start date is available.
Application Process:
Interested candidates should email their CV and a brief statement of research interests to [email protected]. For more information, visit the LinkedIn profile or the lab's announcement.
