Postdoctoral Fellowships in AI & Medical Imaging

University of Pennsylvania Perelman School of Medicine; Meadville (Crawford County); Sur place; USD 52 000 - 68 000; Plein temps

Résumé du poste

The University of Pennsylvania, Perelman School of Medicine, invites applications for three NIH-funded Postdoctoral Fellows to advance research at the intersection of machine learning, medical imaging, and clinical data science.

Fellows will work under Faculty Mentor Yong Fan, PhD in Radiology, contributing to neuroimaging, deep learning for functional networks, and radiomics for CT/urology with multi-site modeling.

Qualifications

• Ph.D. in Computer Science, Biomedical Engineering, Applied Mathematics, Neuroscience, or related quantitative field.
• Strong track record of independent research and publication.

Responsabilités

• Conduct NIH-funded research in ML, medical imaging, and data science.
• Publish in top-tier venues and collaborate with clinical data teams.
• Develop and validate new imaging biomarkers and predictive models.

Connaissances

Machine learning

Deep learning

Computer vision

Medical image analysis

Formation

Ph.D. in Computer Science

Ph.D. in Biomedical Engineering

Ph.D. in Applied Mathematics

Ph.D. in Neuroscience

Ph.D. in related quantitative field

Description du poste

University of Pennsylvania: Postdoctoral Positions: Perelman School of Medicine Postdoctoral

Location

University of Pennsylvania - Perelman School of Medicine

Open Date

Mar 26, 2026

Description

• Faculty Mentor: Yong Fan, PhD (Yong Fan, Ph.D. – AIBIL)
• 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.