Computational Biology Intern

Genmab • Princeton, FL

1d • Hybrid

About The Position

At Genmab, we are dedicated to building extra[not]ordinary® futures, together, by developing antibody products and groundbreaking, knock-your-socks-off KYSO antibody medicines® that change lives and the future of cancer treatment and serious diseases. We strive to create, champion and maintain a global workplace where individuals’ unique contributions are valued and drive innovative solutions to meet the needs of our patients, care partners, families and employees. Our people are compassionate, candid, and purposeful, and our business is innovative and rooted in science. We believe that being proudly authentic and determined to be our best is essential to fulfilling our purpose. Yes, our work is incredibly serious and impactful, but we have big ambitions, bring a ton of care to pursuing them, and have a lot of fun while doing so. Does this inspire you and feel like a fit? Then we would love to have you join us! We are seeking a highly motivated Computational Biology Intern to join our Translational Research team. This role is focused on the high-resolution characterization of the tumor microenvironment using multi-omic clinical datasets. You will work at the interface of bioinformatics and clinical genomics, contributing to the development of analytical frameworks that transform raw sequencing outputs into actionable biological insights. The successful candidate will be responsible for the technical interrogation of high-dimensional data from WES, RNA-seq, ctDNA, and Spatial Transcriptomics platforms. A key focus of this role is the "signal-to-noise" challenge: identifying and mitigating technical artifacts in clinical samples to ensure data integrity for downstream biomarker discovery.

Requirements

• Currently pursuing a BSc, MSc, or PhD in Computational Biology, Bioinformatics, Genomics, or a related quantitative field.
• Advanced proficiency in Python (Pandas, BioPython) and/or R (Tidyverse/Bioconductor).
• Experience with Bash is required.
• Solid understanding of the NGS lifecycle, including library preparation artifacts, alignment algorithms, and variant calling principles.
• A strong interest in data hygiene, systems biology (PPI networks), and the unique engineering challenges associated with clinical oncology datasets.

Responsibilities

• Technical QC Analysis: Evaluate and automate the extraction of high-dimensional quality metrics (e.g., library complexity, UMI consensus efficiency, and fragment size distribution) to distinguish between technical noise and true biological signal.
• Pipeline Optimization: Utilize Python, R, and Bash to refine genomics workflows, ensuring standard outputs (BAM, VCF, and count matrices) are optimized for sensitivity, specifically in low-input ctDNA and FFPE samples.
• Feature Extraction: Develop scripts to structure and tabulate molecular features for use in predictive models of therapy response.
• Network & Pathway Analysis: Integrate DNA and RNA sequencing data to map the functional consequences of oncogenic mutations. Apply systems biology approaches to correlate mutational status with transcriptomic pathway perturbations and protein-interaction networks.
• Spatial Transcriptomics: Support the processing and quality assessment of 10x Visium HD data to characterize the spatial architecture of tumor-immune interactions.
• Immuno-Genomics: Contribute to the characterization of the immune landscape within clinical cohorts, utilizing gene expression signatures to evaluate immune cell infiltration and activation.
• Workflow Development: Maintain high-standard, version-controlled code (Git) within a Unix/Linux environment to ensure all data engineering steps are transparent and reproducible.
• Reporting: Synthesize complex technical metrics into concise data summaries for cross-functional teams of computational and translational scientists.

What This Job Offers

• Career Level: Intern
• Number of Employees: 1,001-5,000 employees