Please email required application materials to:
Deputy Director, PHIND Center at Stanford
Ryan Spitler, PhD
Department of Radiology
Phone: (650) 721-3291
A postdoctoral position is available in the Precision Health and Integrated Diagnostics Center at Stanford (PHIND) headed by Dr. Sam Gambhir MD PhD. The goals of the PHIND Center are to develop, test, and disseminate the next generation of healthcare mechanisms for precision health such as wearable/implantable technologies, data analytics and computational tools including clinical decision making, (molecular) imaging strategies, disease models, fundamental studies on the biology of disease formation, biomarker research and health economics. More recently the PHIND Center has been working as part of Project Baseline—the most in-depth study of human health to date—to map human health. The Center is investigating the transition from health to disease and creating health risk models using multiple forms of data including medical imaging, –omics, biological samples, surveys, EMR, specialized sensors, and other data sources. This exciting work is in collaboration with top scientists at Stanford, Duke, and Verily. The successful candidate will research a variety of topics that may include: automated image annotation using supervised methods of processing associated radiology reports using work embeddings and related methods; developing methods of analyzing longitudinal EMR data to predict clinical outcomes; creating multi-modal deep learning models to integrate multi-dimensional data associated with disease phenotypes; and apply AI and/or other analytic methods applicable to large diverse and complex data sets. Experience in studying large biomedical population data sets and testing multiple hypotheses about populations is preferred.
- The candidate must have a post-graduate degree (PhD or MD) in biomedical data science, informatics, computer science, engineering, statistics, computational biology, applied mathematics or related field.
- The candidate should have experience in machine learning (supervised and unsupervised methods) and AI;
- proficient in Python (NumPy, SciPy, Pandas, etc.) and/or R;
- exploratory and statistical analysis (such as linear models, multivariate analysis, predictive modeling, and stochastic models);
- experience extracting and cleaning data sets.
- Familiarity with deep learning frameworks (TensorFlow, PyTorch, etc.)
- Strong record of distinguished scholarly achievement.
- Outstanding communication and presentation skills with fluency in spoken and written English.
- Three letters of recommendation