Postdoctoral Fellow-MSH-32910-027
About The Position
We are seeking a postdoctoral fellow to focus on time-series modeling and wearable/mobile health data in the context of women’s health. The fellow will design and evaluate AI methods for high-frequency, longitudinal, and real-world data streams, with the goal of enabling translational insights in collaboration with clinicians and data scientists. 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.
Requirements
- PhD in computer science, biomedical informatics, electrical engineering, statistics, or related field.
- Strong expertise in time-series modeling, wearable/mobile health analytics, or longitudinal data analysis.
- Proficiency in Python and PyTorch.
- Demonstrated publication record in ML/AI or computational health.
- Strong communication and teamwork skills.
Nice To Haves
- Experience with clinical or biomedical datasets.
- Familiarity with FHIR standards.
- Background in multimodal learning or representation learning.
Responsibilities
- Develop and refine machine learning methods for wearable, mobile health, and longitudinal data.
- Design robust approaches to handle missingness, irregular sampling, and multimodal integration.
- Collaborate with clinical partners to ensure models are interpretable and clinically actionable.
- Lead manuscripts, presentations, and contributions to open-source pipelines.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
