Postdoctoral Fellow, Computational Immuno-Oncology and Immune-Related Adverse Events
Core
Investigate the interface of cancer genomics, systemic physiology, and immuno-oncology using large-scale, multimodal datasets to define, predict, and understand immune-related adverse events arising from cancer immunotherapy.
Role type
Postdoctoral Fellow, Computational Immuno-Oncology
Builds
Computational phenotyping using natural language processing and large language models; germline genetic analyses including GWAS, HLA, and polygenic risk; multimodal modeling to identify predictors of toxicity.
Domain
Biomedical research, Immuno-oncology, Cancer genomics
Deliverable
production ML models
Required skills
Natural language processing, Large language models, GWAS, Statistical genetics, Germline genomics, Immuno-oncology, Computational analysis, Quantitative reasoning, Scientific writing
Preferred skills
Textual analysis, Longitudinal clinical data analysis, Multidisciplinary collaboration
Technologies
Natural language processing, Large language models, GWAS tools, HLA analysis tools
Responsibilities
Integrate longitudinal clinical text, germline genetics, treatment exposures, laboratory measurements, and patient outcomes to characterize immune toxicity; Develop projects aligned with expertise in computational phenotyping, genetic analyses, or multimodal modeling; Work closely with computational scientists, geneticists, oncologists, immunologists, and clinicians to address clinically relevant questions.
Seniority
Postdoctoral Fellow, Research
