Post Doctoral Research Fellow- Biostatistics and Spatial Transcriptomics

About Us:     The people of Memorial Sloan Kettering Cancer Center (MSK) are united by a singular mission: ending cancer for life. Our specialized care teams provide personalized, compassionate, expert care to patients of all ages. Informed by basic research done at our Sloan Kettering Institute, scientists across MSK collaborate to conduct innovative translational and clinical research that is driving a revolution in our understanding of cancer as a disease and improving the ability to prevent, diagnose, and treat it.

MSK is dedicated to training the next generation of scientists and clinicians, who go on to pursue our mission at MSK and around the globe. Exciting Opportunity at MSK: Computational Scientist The Adusumilli Lab, in close collaboration with the Department of Epidemiology and Biostatistics, is seeking a highly motivated and talented PhD-level Computational Scientist to drive cutting-edge translational cancer research.

This unique joint role sits at the intersection of rigorous statistical methodology, advanced single-cell/spatial omics, and clinical translation in surgical oncology. You will develop and apply innovative statistical frameworks to map the tumor immune microenvironment (TIME), decode multi-cellular architectural niches, and collaborate closely with a multidisciplinary team of computational biologists, statisticians, and surgeons.

Key Requirements Education: PhD in Biostatistics, Statistics, Computational Biology, Bioinformatics, or a closely related quantitative field with strong core statistical training. Technical Expertise: Proven track record in analyzing and modeling single-cell and high-plex spatial profiling data. Direct experience processing data from multiplexed imaging or sequencing-based spatial platforms (e.g., Imaging Mass Cytometry (IMC) , 10x Genomics Xenium, NanoString CosMx, and/or 10x Genomics Visium).

Advanced proficiency in Python and/or R programming and expert-level familiarity with single-cell and spatial ecosystems, specifically Seurat, Giotto, and relevant Bioconductor packages. Strong foundation in spatial statistics, including spatial autocorrelation, cell deconvolution algorithms, and distance-based neighborhood modeling. Experience: Demonstrated experience working with paired single-cell RNA-seq and V(D)J/TCR sequencing datasets.

Experience analyzing translational data involving tumor-infiltrating lymphocytes (TILs), immunophenotyping markers, and clinical-pathological correlation. Ability to bridge the gap between complex mathematical/algorithmic theory and practical clinical interpretation. Soft Skills: Outstanding communication skills, with a proven ability to collaborate effectively across multidisciplinary teams of clinicians, statisticians, and computational biologists.

Core Skills Methodological & Pipeline Development: Develop, scale, and implement novel statistical methods and machine learning frameworks tailored for high-dimensional spatial omics and single-cell landscapes. This includes building pipelines for multiplexed spatial imaging data and paired single-cell multi-omics. Spatial Architecture & Niche Profiling: Implement advanced spatial point pattern analysis, cell-cell interaction modeling, and neighborhood/niche identification to map cell-to-cell spatial proximity and architectural differences within the tumor microenvironment across distinct clinical cohorts.

Immunophenotyping & Clinical Association: Design analytical strategies to model the landscape of the immune microenvironment (e.g., PD-L1 expression patterns, tumor-infiltrating lymphocyte densities) and statistically associate these spatial phenotypes with clinical outcomes. Study Design: Provide expert statistical guidance on study design, sample size estimation, and power calculations for translational protocols and grant proposals (NIH/NCI).

We offer a competitive salary and benefits package, as well as the opportunity to work in a highly collaborative and innovative environment