The fellow will focus on developing and applying computer vision and multimodal AI methods to advance women’s health
Job Summary
The fellow will focus on developing and applying computer vision and multimodal AI methods to advance women’s health.
Collaborate with clinicians and technical researchers to translate methods into clinically relevant tools.
You will have access to unique resources including Mount Sinai’s genome-linked EHR biobank (the Sinai Million), AIRMS (AI-ready Mount Sinai Integrated Data and Analytics Platform), the Minerva HPC cluster, and eHive, a digital platform for wearable and real-world data collection.
Matching Summary
The fellow will focus on developing and applying computer vision and multimodal AI methods to advance women’s health.
Skills & Requirements
Must-have
Computer vision and multimodal AI methods
Develop and adapt deep learning methods
Collaborate with clinicians and researchers
Python and PyTorch proficiency
Publication record in ML/AI
Nice-to-have
Interest in women's health
Translational research experience
Open lab culture contribution
Key Requirements
PhD in computer science, biomedical engineering, electrical engineering, or related field
Strong background in computer vision, medical imaging, or multimodal deep learning