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

Memorial Sloan Kettering Cancer Center • New York, NY

11d • $72,000 - $75,000 • Onsite

About The Position

This postdoctoral fellowship position is within the Reznik Laboratory at Memorial Sloan Kettering Cancer Center (MSK), focusing on research at the intersection of cancer genomics, systemic physiology, and immuno-oncology. The role involves utilizing large-scale, multimodal datasets to investigate questions related to immune-related adverse events from cancer immunotherapy. The fellow will lead collaborative projects to define, predict, and understand these events, including distinguishing single-organ from multisystem toxicity. This will involve integrating longitudinal clinical text, germline genetics, treatment exposures, laboratory measurements, and patient outcomes. The position offers the opportunity to develop projects aligned with the fellow's expertise, such as computational phenotyping using NLP and LLMs, germline genetic analyses (GWAS, HLA, polygenic risk), or multimodal modeling to identify predictors and biological mechanisms of toxicity. The fellow will work closely with a multidisciplinary team of computational scientists, geneticists, oncologists, immunologists, and clinicians to address clinically relevant questions.

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.
• 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.

Nice To Haves

• Candidates are not expected to have experience in all of the research areas listed above.

Responsibilities

• 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.

Benefits

• Opportunity to connect computational method development with clinically important questions about cancer immunotherapy using rich longitudinal clinical and genomic data.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree