Postdoctoral Fellowships in AI & Medical Imaging

University of Pennsylvania; Philadelphia (Philadelphia County); On-site; USD 65,000 - 85,000; Full time

Job summary

A prestigious university in the United States is seeking highly motivated Postdoctoral Fellows for AI & Medical Imaging. This NIH-funded position requires a strong background in machine learning and medical image analysis. Candidates should hold a Ph.D. in relevant fields and demonstrate a track record of independent research. Successful applicants will work on innovative projects at the intersection of machine learning and clinical data science, offering a flexible and collaborative research environment. Applications should include a CV, research statement, and references.

Qualifications

• Strong background in machine learning, deep learning, computer vision, and medical image analysis.
• Proven ability to conduct independent research and publish in top-tier venues.
• Interest in developing Agentic AI applications for medical imaging.
• Proven ability to conduct independent research, collaborate effectively, and publish in top-tier venues.

Responsibilities

• Drive innovative research at the intersection of machine learning and medical imaging.
• Develop advanced statistical and ML methods for neuroimaging.
• Utilize computer vision for automated segmentation and classification.
• Collaborate across teams and publish research findings in top venues.

Skills

Machine learning

Deep learning

Computer vision

Medical image analysis

Education

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

Job description

Overview

Postdoctoral Fellowships in AI & Medical Imaging

Details

• Location: University of Pennsylvania - Perelman School of Medicine
• Open Date: Mar 26, 2026
• Deadline:
• Faculty Mentor: Yong Fan, PhD
• Department: Radiology
• Funding Source: NIH
• Number of positions: 3

We are recruiting three highly motivated Postdoctoral Fellows for NIH-funded positions to drive innovative research at the intersection of machine learning, medical imaging, and clinical data science. We offer a flexible, highly collaborative environment where fellows can dive deep into a specific domain or pioneer research across multiple fields.

Key Research Areas

• Neuroimaging & Predictive Modeling: Develop advanced statistical and ML methods for large-scale neuroimaging to predict brain age, model disease trajectories, and discover neurological biomarkers.
• Functional Network Modeling & Deep Learning: Apply novel DL architectures and generative models to decode dynamic functional connectivity and brain network organization.
• CT Analysis & Urological Applications: Utilize computer vision and radiomics for automated segmentation, classification, treatment response prediction, and robust multi-site modeling in urological health.

Qualifications

• Education: Ph.D. in Computer Science, Biomedical Engineering, Applied Mathematics, Neuroscience, or a related quantitative field.
• Core Expertise: Strong background in machine learning, deep learning, computer vision, and medical image analysis.
• Agentic AI Focus: A strong interest in, or willingness to learn and develop, Agentic AI applications to enhance medical imaging workflows and research.
• Track Record: Proven ability to conduct independent research, collaborate effectively, and publish in top-tier venues.

Application Instructions

• Required documents for upload: Curriculum Vitae (CV), Research Statement (Highlighting your preferred research areas and how your expertise aligns with or bridges them), and References: Names and contact information for three professional references (requests will be sent directly through Interfolio)

To apply, visit https://apply.interfolio.com/183891

Equal Employment Opportunity

The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.