Cambridge, Massachusetts, United States of America
Base: $95,000; bonus/equity: not specified; benefi...
Hybrid
Single-cell and spatial transcriptomics
Machine-learning methods
R and/or python programming
The successful candidate will work closely with experimental and clinical collaborators, applying cutting-edge statistical and machine-learning approaches to identify immune cell states, biomarkers, and mechanisms underlying response to cancer therapy
Job Summary
The successful candidate will work closely with experimental and clinical collaborators, applying cutting-edge statistical and machine-learning approaches to identify immune cell states, biomarkers, and mechanisms underlying response to cancer therapy.
Perform comprehensive computational analyses of single-cell and spatial transcriptomics datasets to characterize the tumor microenvironment in bladder cancer.
Lead the synthesis of computational findings into clear, compelling scientific narratives and drive manuscript preparation for submission to high-impact journals.
Matching Summary
The successful candidate will work closely with experimental and clinical collaborators, applying cutting-edge statistical and machine-learning approaches to identify immune cell states, biomarkers, and mechanisms underlying response to cancer therapy.
Salary
Base: $95,000; Bonus/Equity: Not specified; Benefits: medical, dental, vision, life insurance, disability, retirement plan, savings plan, vacation, sick time, holiday pay, work/personal/family time
Skills & Requirements
Must-have
single-cell and spatial transcriptomics
machine-learning methods
R and/or Python programming
multi-modal datasets integration
tumor microenvironment analysis
Nice-to-have
tumor immunology experience
cancer biology knowledge
translational research environments
imaging-based analyses familiarity
Key Requirements
PhD in related quantitative discipline
Strong experience analyzing high-dimensional omics data