Tech Stack

Description

You will evaluate computational strategies for resolving tumor and tumor microenvironment states from transcriptomic data in oncology, benchmarking deconvolution methods and single-cell analysis best practices to study treatment response and resistance to targeted therapies.

Requirements

• Pursuing BS or MS in Computational Biology, Bioinformatics, Systems Biology, or related field
• Proficiency in R and Python
• Experience analyzing RNA-seq data (bulk and/or single-cell) preferred
• Experience with Seurat, Scanpy, or scVI preferred
• Familiarity with tumor microenvironment biology preferred

Responsibilities

• Review leading bulk and spatial deconvolution tools and benchmark selected methods using curated datasets
• Assess robustness in detecting immune shifts, resistant tumor states, and TME remodeling
• Compare normalization, integration, and batch correction strategies in single-cell analysis
• Evaluate clustering robustness and annotation reproducibility
• Assess impact of processing steps on biological interpretation