Memorial Sloan Kettering Cancer Center

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: Postdoctoral Fellow in Computational Immuno-Oncology and Immune-Related Adverse Events

Join the Reznik Laboratory within MSK’s Computational Oncology Service, investigating questions at the interface of cancer genomics, systemic physiology, and immuno-oncology using large-scale, multimodal datasets. Lead collaborative projects to define, predict, and understand immune-related adverse events arising from cancer immunotherapy, including the factors that distinguish single-organ from multisystem toxicity. Integrate longitudinal clinical text, germline genetics, treatment exposures, laboratory measurements, and patient outcomes to characterize who develops immune toxicity and how these events evolve over time. Develop projects aligned with your expertise, including computational phenotyping using natural language processing and large language models; germline genetic analyses, including GWAS, HLA, and polygenic risk; or multimodal modeling to identify predictors and potential biological mechanisms of toxicity. Work closely with computational scientists, geneticists, oncologists, immunologists, and clinicians to address clinically relevant questions. Key Requirements

A PhD, completed or nearing completion, in a computational or quantitative discipline, including computational biology, bioinformatics, computer science, biomedical informatics, statistical genetics, biostatistics, statistics, engineering, or a related field. Expertise in at least one of the following areas: textual analysis, natural language processing, or large language models; GWAS, statistical genetics, or germline genomics; or immune-related adverse events or immuno-oncology. A strong interest in applying computational approaches to understand cancer treatment toxicity and improve patient care. Enthusiasm for learning new methods and collaborating across disciplines. Candidates are not expected to have experience in all of the research areas listed above. Strong quantitative reasoning and computational analysis skills. Ability to develop rigorous, reproducible analyses of complex biomedical datasets. Ability to formulate research questions and lead projects independently while contributing to collaborative work. Clear scientific writing and communication across computational and clinical disciplines.

Learn more about working as a Post-Doctoral at MSK: This position offers the opportunity to connect computational method development with clinically important questions about cancer immunotherapy using rich longitudinal clinical and genomic data. The fellow will join a multidisciplinary research community within MSK’s Computational Oncology Service, with close collaboration between quantitative scientists and physicians. Projects can build on the fellow’s existing strengths in language models, statistical genetics, or immuno-oncology while providing opportunities to develop expertise in complementary areas.

Pay Range: 72,000-75,000

MSK is an equal opportunity and affirmative action employer committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, disability, veteran status or any other factor which cannot lawfully be used as a basis for an employment decision.

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