Postdoctoral Researcher in Computational Perception
- University of Pennsylvania
- Philadelphia, PA, USA
- Dec 23, 2025
Job Description
Postdoctoral Researcher in Computational Perception
Location: University of Pennsylvania - School of Medicine
Open Date: Oct 23, 2025
Deadline:
Faculty Mentor: Kevin B. Johnson, MD, MS
Department: Biostatistics, Epidemiology, and Informatics
Funding Sources: Grant
Number of Positions: 1
Open to applications from U.S. citizens and foreign nationals.
The Artificial Intelligence for Ambulatory Care Innovation (AI-4-AI) lab (https://www.med.upenn.edu/kbjohnsonlab/) is recruiting a postdoctoral researcher to join our lab under the funding of Dr. Kevin Johnson's NIH Pioneer Award. Our lab works at the intersection of artificial intelligence, computer vision, and health innovation, bridging biomedicine, engineering, informatics, and social science to develop and test AI-powered solutions for real-world challenges in the practice of medicine.
Led by Dr. Kevin Johnson, a pioneer in medical information technologies to improve patient care and safety, our lab is housed within the Department of Biostatistics, Epidemiology, and Informatics at Penn's Perelman School of Medicine.
The core of research in the AI-4-AI lab centers around the study of socio-technical ethnography, focusing on how technology is used, where in the care process it should be used, and how to effectively implement its use in ambulatory care.
Duties & Responsibilities:
Our lab has created a unique repository of clinical encounter data, the Observer Repository. This repository contains multi-modal data (video, audio, transcripts, EHR logs) from ambulatory care visits. This position offers to opportunity to conduct innovative work in fine-tuning AI-powered vision models using our visitome repository to detect medical risks in primary care.
As a post-doctoral researcher, you will be expected to lead independent research while working collaboratively with our team and our clinical partners. You will be writing peer-reviewed publications for research conferences and journals, giving presentations at research conferences, and assisting in grant proposal development. You will be responsible for mentoring undergrad and graduate students.
The initial appointment will be for 1-2 years, with the possibility of renewal based on performance and funding availability.
The University of Pennsylvania is an EOE. Minorities/Women/Individuals with disabilities/Protected Veterans are encouraged to apply.
Salary: Competitive, commensurate with NIH postdoctoral guidelines.
Qualifications
Required:
- Ph.D. degree in computational perception, computer vision, or closely related fields
- Demonstrated proficiency in AI/machine learning, including excellent programming skills in Python and experience with deep learning frameworks
- Demonstrated expertise in computer vision techniques and algorithms
- Strong publication record in relevant venues
- Excellent writing and oral communication skills
Preferred:
- Experience with healthcare applications of computer vision or AI
- Experience with large-scale vision datasets and video processing
- Knowledge of privacy-preserving techniques and de-identification methods
- Interested in translational research and clinical applications
- Experience with multi-modal data fusion and analysis
Application Instructions
Applications should be sent through Interfolio using the link below and include:
- Cover Letter
- Curriculum vitae (CV)
- Research Statement
- Contact Information for three references
Review of applications will be done on a rolling basis and will continue until the position is filled.
Questions?
Please contact Karen O'Connor, Associate Director, with any questions about the position or application process (karoc@pennmedicine.upenn.edu)
To apply, visit https://apply.interfolio.com/176460
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.
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