





In this internship, you will dive into a large-scale colorectal cancer dataset (~1800 samples) and uncover the molecular patterns that distinguish different tumor types. Using RNA-seq, mutation, and copy number data, you’ll explore how tumors differ between patients, between primary and metastatic sites, and even before and after treatment. You will be applying existing molecular frameworks such as CMS, CRIS, and CIN70, and combining these with genomic alterations to build a multi-dimensional view of tumor biology. The goal is not just to analyze data, but to discover meaningful biological structure, and test whether these patterns relate to real patient outcomes like progression-free survival. To strengthen your findings, you will validate them in external datasets such as TCGA, connecting your work to the broader scientific landscape. This project sits at the intersection of biology, data science, and medicine, and offers a hands-on opportunity to work with real-world clinical data while developing skills in computational analysis, statistical thinking, and translational research. Does this sound like something that interests you? We look forward to receiving your application and exploring the possibility of you joining our team to contribute to the advancement of antibody therapeutics! Help us get to know you by submitting a CV and motivational letter.