We are seeking a highly motivated postdoctoral fellow to join our new interdisciplinary lab at Mount Sinai. The fellow will focus on developing and applying computer vision and multimodal AI methods to advance women’s health. The project leverages unique clinical, imaging, and genomic datasets, and the fellow will work closely with both computational and clinical mentors to design and validate translational AI tools.

• Develop and adapt computer vision and multimodal deep learning methods for medical data (radiology, pathology, genomics, EHR).
• Collaborate with clinicians and technical researchers to translate methods into clinically relevant tools.
• Lead and co-author manuscripts, conference presentations, and grant proposals.
• Mentor students and contribute to building a collaborative, open lab culture.

Requirements

• PhD in computer science, biomedical engineering, electrical engineering, or related field.
• Strong background in computer vision, medical imaging, or multimodal deep learning.
• Proficiency in Python and PyTorch.
• Demonstrated publication record in ML/AI or computational health.
• Strong communication and collaboration skills.

Preferred

• Experience with clinical or biomedical datasets.
• Familiarity with OMOP Common Data Model.
• Interest in women’s health and translational research.

Environment

Heavy menstrual bleeding affects nearly one in three women of reproductive age and is a leading cause of iron deficiency worldwide. Yet it remains one of the most under-recognized challenges in medicine. Our lab at the intersection between the Artificial Intelligence and Human Health Department and the Department for Obstetrics, Gynecology and Reproductive Sciences at Mount Sinai has been awarded a Wellcome Leap Missed Vital Sign grant to change this.

We are building a new, interdisciplinary group at the intersection of AI, human health, and obstetrics & gynecology. Our mission is to harness state-of-the-art methods in machine learning and multimodal data integration to close critical gaps in women’s health—and to translate these advances into solutions that matter for patients and clinicians.

As a founding member, you will help shape a lab designed for openness, collaboration, and translation. 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. Partnerships with the Hasso Plattner Institute in Germany create further opportunities for international collaboration.

This is a chance to join at the ground level of a lab committed to impact: bringing computational innovation directly into women’s health.