Faculty Position - Department of Epidemiology and Cancer Control, Data Science
Core
Lead a research program applying state-of-the-art computational methods to extract insights from high-dimensional, multimodal health data and develop predictive models for childhood cancer survivorship care.
Role type
Faculty data scientist (Full, Associate, or Assistant level)
Builds
Predictive and integrative models for survivorship care
Domain
Pediatric oncology / Biomedical data science
Deliverable
production ML models
Required skills
Advanced machine learning (deep learning, representation learning, probabilistic modeling), Natural language processing and large language models, AI-driven multimodal data integration, Computational pipelines for large-scale biomedical datasets, Causal inference, Risk stratification, Precision medicine, Feature engineering, Pretrained foundation models or self-supervised learning, AI agents for data orchestration
Preferred skills
Experience with wearable sensor data, Image/video phenotyping, Multi-omics data, Physiological signals, Longitudinal clinical assessments, Usability evaluation, Geographical and spatiotemporal data
Technologies
Deep learning frameworks, NLP libraries, Multi-omics analysis tools, AI orchestration platforms
Responsibilities
Apply computational methods to high-dimensional health data, Develop predictive models for survivorship care, Lead a vibrant research program at the intersection of data science and clinical/biological sciences, Collaborate across Cancer Center Programs with clinic- and laboratory-based researchers, Leverage diverse data streams including sensor data, imaging, multi-omics, and clinical assessments
Seniority
Faculty level (Full, Associate, or Assistant), independent research leadership
