Postdoctoral fellow

Company Name Memorial Sloan Kettering Cancer Center

Memorial Sloan Kettering Cancer Center (MSK) is one of the worlds premier cancer centers, committed to exceptional patient care, leading-edge research, and superb educational programs. The blending of research with patient care is at the heart of everything we do. The institution is a comprehensive cancer center whose purposes are the treatment and control of cancer, the advancement of biomedical knowledge through laboratory and clinical research, and the training of scientists, physicians, and other health care workers.

Memorial Sloan Kettering Cancer Center (MSK) Department of Biostatistics and Epidemiology is seeking applications for a postdoctoral fellow with interests in developing and applying novel statistical methods in the quickly emerging area of data fusion, transfer, and federated learning. The federally funded position will involve conducting research that integrates multiple large-scale healthcare data sources, including electronic health records and clinical trial data, for mediation analysis.

The postdoctoral fellow will work closely with Dr. Andriy Derkach and a multidisciplinary research team to develop innovative statistical methods to address the challenges of integrating large-scale healthcare-derived data sources, including electronic health records and clinical trial data, for mediation analysis. This position will provide exciting opportunities to work on statistical techniques for non- and semi-parametric models with moderate- to high-dimensional mediators while addressing the practical complexities of large, complex studies with multiple data sources used by modern scientists.

The position provides an outstanding opportunity for individuals seeking careers at the intersection of biostatistics, data science, and health research, with opportunities to contribute both methodological and applied research in a highly collaborative environment.

The postdoctoral fellow will be mentored by Dr. Andriy Derkach, and the initial appointment is for 2 years, with potential renewal contingent upon performance and funding availability.

The ideal candidate should have:

• PhD degree in biostatistics, statistics, bioinformatics, or a related field
• Experience in methodology in the areas of missing data, public health studies under various sampling designs, high-dimensional data or causal inference is desired but not required
• Solid methodological training in statistics; be comfortable working with large data sets; proficient in at least one of the statistical programming languages R/Matlab/Python; and have experience working on Unix/Linux systems and basic shell scripting