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Postdoctoral Fellow
Wearables and Time-series Modeling Full-time
Description
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.
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.
Qualifications
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.
Preferred
- Experience with clinical or biomedical datasets.
- Familiarity with FHIR standards.
- Background in multimodal learning or representation learning.