Postdoctoral Fellow, Computational Immuno-Oncology
Investigate cancer immunotherapy and immune-related adverse events using large-scale multimodal and genomic datasets.
Best for:Best for computational biologists and quantitative researchers with a strong background in genomics, NLP, or oncology data analysis.
Overview
Memorial Sloan Kettering Cancer Center is seeking a Postdoctoral Fellow to join the Reznik Laboratory within the Computational Oncology Service. The fellow will lead collaborative research projects focusing on the intersection of cancer genomics, systemic physiology, and immuno-oncology. Responsibilities include analyzing large-scale multimodal data, integrating longitudinal clinical records, and applying natural language processing or statistical genetics. The ideal candidate will work closely with multidisciplinary teams of computational scientists, clinicians, and oncologists to improve patient care.
What you'll do
- Lead collaborative research projects to define, predict, and understand immune-related adverse events from cancer immunotherapy.
- Integrate longitudinal clinical text, germline genetics, treatment exposures, and patient outcomes.
- Develop computational phenotyping approaches using natural language processing and large language models.
- Conduct germline genetic analyses including GWAS, HLA typing, and polygenic risk score calculations.
- Collaborate closely with computational scientists, geneticists, oncologists, and immunologists to address clinical questions.
- Communicate research findings through clear scientific writing and presentations across disciplines.
Requirements
Degree required: PhD in a computational or quantitative discipline, including computational biology, bioinformatics, computer science, biomedical informatics, statistical genetics, biostatistics, statistics, or engineering
Language: English; excellent written and verbal proficiency
Instruction: English
- Expertise in textual analysis, natural language processing, or large language models
- Experience with GWAS, statistical genetics, or germline genomics
- Strong quantitative reasoning and computational analysis skills
- Ability to develop rigorous, reproducible analyses of complex biomedical datasets
- Demonstrated ability to formulate research questions and lead projects independently
- Clear scientific writing and communication skills across computational and clinical disciplines
- Candidates must have completed or be nearing completion of a PhD.
Funding & terms
How to apply
Deadline: 2026-12-27
Interested applicants should submit their curriculum vitae, a letter of interest, and reference contact information via email directly to the listed supervisors.
- CV
- Letter of interest
- References
Contact
Ziad El Bakouny and Ed Reznik· Supervisors
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