We are seeking a highly motivated postdoctoral fellow with a strong interest in developing new statistical and computational methods in cancer research. Our group collaborates closely with physician scientists to study antitumor immune responses using rich cutting-edge in-house omics datasets. These include single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and hyperplex immunofluorescence (hIF), derived from tissue samples such as melanoma and colorectal cancer. The successful candidate will have the opportunity to lead the development of novel statistical and computational approaches while collaborating with world-leading oncology experts to drive scientific discoveries through real-world data applications.
About the Institution
Memorial Sloan Kettering Cancer Center (MSK) is one of the world’s 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.
Applicants should hold a Ph.D. degree in computational biology, biostatistics, statistics, bioinformatics, or a related field, ideally with experience in omics data analysis, imaging data analysis, and modern computational platforms (such as PyTorch and Pyro). The ideal candidate should have a strong background in statistics, be comfortable working with large datasets, and be proficient in at least one statistical programming languages (e.g., Python, R). Experience with Unix/Linux systems and basic shell scripting is also required.
To apply, send a cover letter, cv, and the names of 3 references to Dr. Xinjun Wang (wangx11@mskcc.org).