Post-Doctoral Research Fellow

The successful applicant will work closely with Dr. Yingqi Zhao, and other collaborators to develop statistical and machine learning methods for medical decision-making in cancer prevention and early detection. Key areas of focus include, but are not limited to, designing clinical decision rules for cancer early detection using real-world data such as Electronic Medical Records (EMRs), developing AI tools for integrating multimodal data in early detection, and developing EMR-based algorithms to identify high-risk populations. The applicant will have the opportunity to engage in research across a wide range of statistical areas and will gain access to data from various real-world studies. These efforts will result in both methodological and collaborative publications in high-quality, peer-reviewed journals.

• Suitable applicants should complete (or nearly complete) a Ph.D. or equivalent in Statistics, Biostatistics, or a similar quantitative field prior to their appointment.
• The candidate should have solid training in statistical methods and computational skills.
• Experience with real-world data analysis, AI and machine learning, causal inference, and precision medicine are plus.

• CV;
• Two (2) publication samples or preprints;
• Contact information for three (3) references.