Computational Biology Intern

Job Title

Computational Biology Intern

Our internship program provides interns with hands‑on experience and relevant projects that directly align with our company’s goals. Additionally, we believe our program provides a valuable opportunity to learn, thrive, and build a strong network. We encourage you to review our website to learn why we’re always looking for smart, purpose‑led candidates to play a role in our bold, extra[not]ordinary® future.

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.

Genomic Data Engineering & Quality Refinement

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.

Systems Biology & Immune Profiling

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.

Computational Reproducibility

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.

• Currently pursuing a BSc, MSc, or PhD in Computational Biology, Bioinformatics, Genomics, or a related quantitative field.
• Advanced proficiency in Python (Pandas, Bio Python) 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.

Internships will take place June — August 2026. This role will be based in Princeton, NJ and operate on a required hybrid schedule — 3 days in the office and 2 days remote per week. This role is not eligible for sponsorship.

Help us learn about you by submitting a complete and thoughtful application, which includes your resume. Your application and resume are a way for us to initially get to know you, so it’s important to complete all relevant questions to ensure we have as much information about you as possible. Every application matters to us, and we’ll carefully review each submission as quickly as…