Opportunity Details

Department

Department of Radiology

Division

Radiological Sciences

Rank

Commensurate w/Experience

Title

Faculty Cluster Hire in AI-Enabled Imaging Science

Position Description

About C2AIS

Established in 2026 and jointly sponsored by the MIR, the McKelvey School of Engineering, and I2DB (Institute for Informatics, Data Science and Biostatistics), C2AIS (Center for Computational and AI-enabled Imaging Sciences) is a university-wide center for computational and AI-enabled imaging science. It brings together experts in imaging physics, computation, and AI from across WashU’s Medical and Danforth campuses, and partners with WashU Medicine, Arts & Sciences, Siteman Cancer Center, the Bursky School of Public Health, and BJC HealthCare. Its mission spans foundational discovery, clinical validation and deployment, graduate education through the Imaging Science PhD and MS programs, and industry partnership and commercialization. Working alongside established imaging research centers across WashU, C2AIS moves computational imaging from methods to impact. Its work spans medicine, from cancer, cardiovascular disease, and neurological disorders to the biological, physical, and environmental sciences, including microscopy, materials characterization, remote sensing, and astronomical imaging.

https://www.mir.wustl.edu/research/global-centers/c2ais/

Position Summary

C2AIS at Washington University in St. Louis is recruiting three faculty members whose work sits at the intersection of imaging physics, computation, and medicine. 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 hold a primary or joint appointment in the Mallinckrodt Institute of Radiology (with one position jointly appointed in I2DB) and 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. C2AIS faculty receive competitive start-up support, research space, access to high-performance computing and large-scale clinical imaging data, and structured mentoring.

Primary Duties and Responsibilities (Essential Functions)

Develop and lead an independent, externally funded research program in AI-enabled imaging science aligned with one or more C2AIS priority areas.

• Collaborate across imaging science, engineering, and clinical departments to move computational methods toward clinical and scientific impact, including, where appropriate, tools tested and deployed in clinical workflows.
• Publish in leading peer-reviewed venues and pursue extramural funding (e.g., NIH, NSF, foundation, or industry support) to sustain an independent research program.
• Teach and mentor graduate students, postdoctoral fellows, and/or residents; contribute to interdisciplinary coursework connecting imaging science, AI, and clinical medicine.
• Participate in service to the department, center, and university, including committee work and contributions to C2AIS's shared proposals, publications, and community-building activities.
• Engage with C2AIS's broader network — including Washington University's wider AI and data science community — to expand collaboration and computing capacity.

Job Location / Working Conditions

Primary work is based at the Mallinckrodt Institute of Radiology at Washington University in St. Louis (and, for the joint position, I2DB), with direct access to a major academic medical center, large-scale multimodal imaging data, and shared computing resources. Normal office/laboratory environment; may include exposure to clinical/research settings depending on specific role and department.

Basic Qualifications

Research Areas of Interest

Candidates need not fit neatly into a single area; we welcome applicants whose work spans or extends beyond these areas.

• 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.

Multimodal Clinical Data Science (joint MIR–I2DB appointment) — methods that integrate imaging with electronic health record, genomic, and pathology data for longitudinal outcome modeling, causal inference, quantifying the added value of imaging, and federated learning across institutions.

Additional Qualifications

Minimum Education / Experience

PhD, MD, MD/PhD, or equivalent terminal degree in biomedical engineering, computer science, electrical engineering, physics, applied mathematics, medical physics, computational science, statistics or biostatistics, data science, or a related field, completed by the appointment start date.

Required Licensure / Certification / Registration

None.

Preferred Education / Experience

Postdoctoral research experience; a demonstrated publication record in imaging science, machine learning, or computational medicine; experience securing or contributing to extramural research funding; evidence of successful interdisciplinary and/or clinical collaboration.

Special Instructions

Why Washington University in St. Louis

• WashU Medicine unites more than 14,000 faculty, staff, and trainees across 78 subspecialties and more than 130 clinical practices, most based on the Washington University Medical Campus alongside Barnes-Jewish Hospital and St. Louis Children’s Hospital — the academic hospitals of BJC HealthCare, WashU Medicine’s longstanding clinical partner.
• Washington University School of Medicine has ranked second among U.S. medical schools in NIH funding for three consecutive years, with more than $675 million in NIH awards in fiscal year 2025.
• The Mallinckrodt Institute of Radiology ranked second among U.S. academic radiology departments in NIH funding in fiscal year 2025, with dedicated research centers spanning Biomedical MR, Biophotonics, Computational Imaging, Neuroimaging, and Precision Radiotheranostics.
• The McKelvey School of Engineering and WashU’s interdisciplinary Imaging Science PhD and MS programs provide engineering collaborators and a strong pipeline of graduate trainees, and BJC HealthCare’s regional hospital network provides large-scale clinical imaging and health data for research.

Posting Date

10/28/2026

End Date

No end date

EEO Statement

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