Post Doc Associate

Ph.D. in biomedical informatics, computer science, data science, statistics, or related STEM fields.

The AI Postdoctoral Fellow supports the Department of Radiology’s research laboratory by designing and conducting advanced artificial intelligence research in medical imaging. This position focuses on developing clinically meaningful machine learning models, publishing research in high-impact peer-reviewed journals, collaborating with physician-scientists, and advancing translational and grant-funded innovation across the department’s research priorities.

Key responsibilities include:

• Conduct independent and collaborative artificial intelligence research in medical imaging.
• Develop novel machine learning and deep learning methods for clinical, research, and educational applications in radiology.
• Build, train, validate, and optimize AI/ML models using CT, MRI, PET, chest X-ray, and multimodal datasets.
• Preprocess and manage large-scale clinical imaging data, including DICOM and NIfTI files, with careful attention to data integrity, reproducibility, privacy, and regulatory compliance.
• Perform rigorous model evaluation, including external validation, robustness testing, and bias and fairness assessments.
• Lead manuscript preparation and present research findings through peer-reviewed journals and national conferences, including RSNA, MICCAI, SIIM, and ISMRM.
• Support grant proposal development by contributing technical aims, preliminary data, methodological expertise, and innovation narratives.
• Mentor junior trainees, including students, residents, and research assistants, and actively participate in laboratory meetings and collaborative research initiatives.
• Develop and maintain well-documented, reproducible AI pipelines using version control, experiment tracking, and other established best practices.

Salary is commensurate with NIH Guidelines 

Ph.D. in biomedical informatics, computer science, data science, statistics, or related STEM fields.

• Demonstrated experience developing machine learning models, preferably for healthcare or medical imaging applications.
• Strong record of scholarly research, including peer-reviewed publications.
• Experience translating imaging AI into clinical practice, including workflow integration or deployment.
• Experience working with large-scale or multicenter datasets, such as NIH, UK Biobank, or OSIC.
• Specialized expertise in emerging areas such as foundation models, self-supervised learning, federated learning, explainable AI, or multimodal data fusion.
• Understanding of academic medicine and demonstrated potential for long-term leadership in translational AI research.

In order to be considered, you must upload your cover letter and most recent CV.

Application must be submitted by 11:55 p.m. (ET) of the posting end date.