DESCRIPTION

Duke University and North Carolina State University (NC State) invite applications for a full-time Postdoc Associate to conduct research on causal inference and analytic methods for data integration, with a focus on innovative statistical methods that boost the efficiency and robustness of clinical trials by incorporating real-world data and taking into account hidden biases.

A recent FDA U01 grant and other funding sources will fund the position. The position will be primarily based in the Department of Biostatistics & Bioinformatics, Duke University School of Medicine, under the supervision of Dr. Xiaofei Wang. The Postdoc Associate will also work closely with Dr. Shu Yang from the Department of Statistics at NC State and other clinical and methodology investigators from Duke University, Brown University, and Eli Lilly & Company.

Under the guidance of the PIs, co-investigators, and collaborators, the Postdoc Associate will be responsible for developing new statistical methods to empower clinical trials by harnessing external historical controls or another type of auxiliary information from existing clinical trials or real-world data.

The Postdoc Associate will use analytic and Monte Carlo methods to compare the new designs and techniques with existing ones in binary, continuous, and time-to-event outcomes. The Postdoc Associate will help develop R/SAS software for the proposed statistical methods and also work closely with all project investigators to extract relevant data from existing clinical trials, extensive observational studies, or population-based databases from multiple rare and common disease areas, including Alzheimer's, brain tumors, and lung cancers.

The Postdoc Associate will attend the study team's regular face-to-face or virtual meetings and the meetings with the FDA. We expect the Postdoc Associate to be the leading author or a co-author on statistical or medical publications and to disseminate research findings at professional conferences.

QUALIFICATIONS

To forge closer collaboration with statistical and medical investigators, the successful candidate will possess these qualifications:

• Doctoral degree in Statistics, Biostatistics, or related fields
• Strong interest in developing novel statistical methods motivated by medical research needs
• Solid background in causal inference and survival analysis
• Experience with clinical trial research, machine learning, and high-dimensional statistics (desirable but not required)
• Strong statistical computing skills in R and SAS
• Excellent writing and communication skills

Duke aspires to create a community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas-an exchange that is best when the rich diversity of our perspectives, backgrounds, and experiences flourishes. To achieve this exchange, it is essential that all members of the community feel secure and welcome, that the contributions of all individuals are respected, and that all voices are heard. All members of our community have a responsibility to uphold these values.