Postdoctoral Fellow in computational neuroimaging

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Faculty Mentor:

Aristeidis Sotiras, PhD (Aristeidis (Aris) Sotiras, PhD - AIBIL) (https://aibil.med.upenn.edu/people/aristeidis-aris-sotiras-phd/) +

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. 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.

Required documents for upload:

CV, Research statement, at least 3 referencess 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.