Postdoctoral Fellow in computational neuroimaging

University of Pennsylvania Perelman School of Medicine; Pennsylvania; On-site; USD 60,000 - 72,000; Full time

Job summary

Two postdoctoral positions at the Center for Biomedical Image Computing and Analytics (CBICA), Radiology, University of Pennsylvania, are available for research in computational neuroimaging and machine learning. The successful candidate will work on image analysis tools for MRI/PET and ML applications to neurocognitive disorders.

The roles are NIH grant funded, with visa sponsorship welcomed. A PhD in a relevant field is required, and there is a strong emphasis on collaboration across a vibrant

Qualifications

• PhD in computer science, biomedical engineering, electrical engineering, applied mathematics, neuroscience, or related quantitative field.
• Strong background in image processing, statistics, ML, or pattern recognition.
• Experience with deep learning and neuroimaging data (MRI or PET) is highly desirable.

Responsibilities

• Contribute to development of advanced image analysis tools for MRI and PET data.
• Apply machine learning to imaging and clinical data from neurocognitive disorders.
• Collaborate within CBICA radiology to advance brain-health research.

Skills

Image processing

Statistical analysis

Machine learning

Pattern recognition

Deep learning

Neuroimaging data

Education

PhD in a relevant field

Job description

University of Pennsylvania: Postdoctoral Positions: Perelman School of Medicine Postdoctoral
Location
University of Pennsylvania - Perelman School of Medicine
Open Date
Mar 06, 2026
Description

• Faculty Mentor: Aristeidis Sotiras, PhD (Aristeidis (Aris) Sotiras, PhD – AIBIL)
• Department: Radiology
• Funding Source: NIH
• Number of positions: 2

Two postdoctoral positions in computational neuroimaging and machine learning are available at the Center for Biomedical Image Computing and Analytics (CBICA), Radiology, University of Pennsylvania. The successful candidate will join a highly collaborative research environment focused on developing computational methods for analyzing large-scale neuroimaging and clinical datasets to better understand brain health and disease. The postdoctoral fellow will contribute to projects at the intersection of medical image analysis, machine learning, and clinical neuroscience, with opportunities to work in two main research directions: (1) Development of advanced image analysis tools for MRI and PET data to study brain aging and neurodegenerative diseases, including Alzheimer’s disease. These projects involve large multi-cohort datasets and focus on extracting biologically meaningful imaging biomarkers. (2) Application of machine learning methods to imaging and clinical data from patients with neurocognitive disorders, with the goal of improving disease characterization, prediction of clinical outcomes, and personalized diagnostics. NIH grant funded. Applicants requiring visa sponsorship are welcome to apply.
Qualifications
The ideal candidate will have a strong background in image processing, statistical analysis, machine learning, or pattern recognition. Experience with deep learning and neuroimaging data (e.g., MRI or PET) is highly desirable.
Applicants should hold a PhD in computer science, biomedical engineering, electrical engineering, applied mathematics, neuroscience, or a related quantitative field.