Research Associate - Artificial Intelligence in Biomedical Imaging Lab (AIBIL)

Description

The Department of Radiology at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for a Research Associate position in the Academic Support Staff. This appointment will be initially for one (1) year and continuation during that time period and renewal are based on satisfactory performance and availability of funding (limited to three (3) years). Applicants must have a Ph.D. or equivalent degree. A degree in Computer Science and Technology is highly valued and recognized as a significant qualification.

Responsibilities may include working on radiomics and radiogeomics of glioblastoma, using MRI scans and deep learning methods. Experience with such data and methods is essential. The candidate will interact with collaborators from a large international consortium.

The successful applicant will have an opportunity to further skills in collaborative research, and there is potential for independent research to be funded by a sponsored program. Candidates will receive mentorship in grant writing and grant proposals suitable for early-stage academic investigators. This position is designed to serve as a form of professional training to develop candidates into lead independent researchers.

This position offers a unique opportunity to engage in impactful research supported by external grant funding. The candidate will play a vital role in presenting and publishing research findings, as well as crafting grant proposals to secure continued financial support. We highly value candidates with experience in training students in research methodologies, as your expertise will help shape the next generation of researchers.

Equal Employment Opportunity Statement

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.