Faculty Cluster Hire in AI-enabled Imaging Science

The C2AIS vision is to build a center that brings together cutting-edge computation and artificial intelligence to accelerate scientific breakthroughs and improve clinical care. We are currently recruiting a cluster of faculty members to build towards this vision. We seek researchers who develop foundational AI and computational methods for medical imaging and who want to see those methods tested, refined, and deployed in real clinical settings.

Successful candidates will join a growing, cross-school community spanning imaging science, machine learning, engineering, and medicine, with the resources of a major academic medical center and research university behind them.

Our hiring priorities include:

• AI-Enabled Image Formation: learned and physics-informed reconstruction, joint design of image acquisition and reconstruction, generative models for low-dose and accelerated imaging, and emerging modalities such as photon-counting CT.
• Imaging Science Theory and Technology Assessment: mathematical and statistical foundations of imaging and imaging AI, objective task-based assessment of image quality and system performance, and in silico methods such as virtual imaging trials and digital twins for designing, evaluating and supporting regulatory review of imaging technologies and AI tools.
• Foundation Models for Body and Oncologic Imaging: large-scale multimodal and vision–language models for CT, MRI and PET of the chest, abdomen and cancer, developed with MIR clinical divisions and Siteman Cancer Center.
• Clinical Workflow AI: vision–language and agentic AI systems for report generation, protocoling, follow-up management and other radiology workflow tasks, developed and deployed in partnership with BJC HealthCare.
• Image-Guided Intervention: real-time imaging and AI for interventional radiology, surgery and radiation therapy, including closed-loop guidance and intraprocedural decision support.
• Trustworthy and Deployable Imaging AI: uncertainty quantification, robustness and generalization, and post-deployment performance monitoring that carry AI tools from research into clinical workflows and commercialization pathways.