Post Doctoral Research Fellow/Computational Scientist, Biostatistics & Spatial Transcriptomics -Biostatistics & Surgery Memorial Sloan Kettering Cancer Center
About The Position
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.
Requirements
- PhD in Biostatistics, Statistics, Computational Biology, Bioinformatics, or a closely related quantitative field with strong core statistical training.
- 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.
- 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.
- Outstanding communication skills, with a proven ability to collaborate effectively across multidisciplinary teams of clinicians, statisticians, and computational biologists.
Responsibilities
- 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.
- 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.
- 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.
- Provide expert statistical guidance on study design, sample size estimation, and power calculations for translational protocols and grant proposals (NIH/NCI).
Benefits
- competitive salary and benefits package
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
