Postdoc: AI-Driven Medical Imaging & Neuroimaging Research

University of Pennsylvania Perelman School of Medicine; Pennsylvania; On-site; USD 55,000 - 70,000; Full time

Job summary

University of Pennsylvania Perelman School of Medicine is recruiting three postdoctoral fellows to advance NIH-funded research at the intersection of machine learning, medical imaging, and clinical data science. The roles sit in Radiology, offering a collaborative environment for deep dives into neuroimaging, DL, and radiomics.

Candidates will focus on brain age prediction, disease trajectory modeling, and multi-site imaging studies, with opportunities to publish and contribute to cutting-edge

Qualifications

• Ph.D. in Computer Science, Biomedical Engineering, Applied Mathematics, Neuroscience, or related quantitative field.
• Strong background in ML, DL, CV, and medical image analysis.
• Agentic AI focus or willingness to develop Agentic AI applications in imaging workflows.
• Proven ability to conduct independent research, collaborate effectively, and publish in top-tier venues.

Responsibilities

• Develop advanced ML methods for large-scale neuroimaging to predict brain age and disease trajectories.
• Apply DL architectures and generative models to decode functional connectivity and brain networks.
• Utilize computer vision and radiomics for CT analysis and multi-site modeling in urological health.
• Collaborate with colleagues to advance imaging workflows and research outcomes.

Skills

Machine learning

Deep learning

Computer vision

Medical image analysis

Research independence

Publications

Education

Ph.D. in Computer Science, Biomedical Engineering, Applied Mathematics, Neuroscience, or related quantitative field

Job description

University of Pennsylvania Perelman School of Medicine is recruiting three postdoctoral fellows to advance NIH-funded research at the intersection of machine learning, medical imaging, and clinical data science. The roles sit in Radiology, offering a collaborative environment for deep dives into neuroimaging, DL, and radiomics.

Candidates will focus on brain age prediction, disease trajectory modeling, and multi-site imaging studies, with opportunities to publish and contribute to cutting-edge