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Memorial Sloan Kettering Cancer Center in New York, NY invites applications for a Postdoctoral Fellow in Biostatistics. The role focuses on developing novel statistical and computational methods for cancer research using in-house omics datasets.
The successful candidate will lead methodological development and collaborate with oncology experts, with co-mentors Dr. Ronglai Shen and Dr. Xinjun Wang, leveraging Python, R, PyTorch, and modern computational platforms.
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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.
We are seeking a highly motivated postdoctoral fellow with a strong interest in developing novel statistical and computational methods for cancer research. Our group works closely with physician-scientists to investigate diverse cancer-related questions, including antitumor immune responses and tumor state differentiation, using rich, cutting-edge in-house omics datasets. These datasets include single-cell RNA sequencing, spatial transcriptomics (Visium/VisiumHD, Xenium), and hyperplex immunofluorescence data derived from tissue samples from melanoma, breast cancer, colorectal cancer, and other malignancies.
The successful candidate will have the opportunity to lead the development of innovative statistical and computational approaches while collaborating with world-leading oncology experts to drive scientific discoveries through real-world data applications. The position will be co-mentored by Dr. Ronglai Shen, Chief Attending Biostatistician, and Dr. Xinjun Wang, Assistant Attending Biostatistician.
Applicants should hold a Ph.D. degree in biostatistics, statistics, computational biology, bioinformatics, or a related field, ideally with experience in omics data analysis, imaging data analysis, and modern computational platforms (such as PyTorch, Pyro, NumPyro). A strong background in statistics, the ability to work with large-scale datasets, and proficiency in at least one statistical programming language, such as Python or R, are required. Experience with Unix/Linux systems and basic shell scripting is also expected.