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.

Tagged as: Computer Science, Data Science