Histopathology Imaging Data Science Intern

Genmab•Princeton, TX

33d•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! 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 looking for a motivated and detail-oriented Digital Pathology Intern to join our team for a summer internship. This role will focus on developing AI models to analyze H&E and IHC images and multiomics data (such as RNAseq and DNA mutations) to characterize the tumor microenvironment and uncover biomarkers linked to disease biology and treatment efficacy. The intern will collaborate with our translational data science team and other stakeholders to contribute to innovative translational research in cancer treatment.

Requirements

Currently enrolled in a degree program in Computer Science, Bioinformatics, Data Science, or a related field.
Proficiency in programming languages such as Python or R.
Experience with machine learning frameworks (e.g., PyTorch) and data analysis tools.
Familiarity with image processing and analysis techniques.
Experience with cloud computing platforms, e.g., AWS, DataBricks.
Ability to collaborate effectively in a team setting.
Demonstration of initiative, a proactive attitude, and eagerness to learn in a fast-paced environment.

Nice To Haves

Prior experience with digital pathology or multiomics data analysis.
Basic understanding of cancer biology or oncology research.
Excellent written and verbal communication skills.
Familiarity with AI coding/agent tools (e.g., Cursor).

Responsibilities

Data ingestion & Analysis Develop or utilize pathology-specific foundation models e.g., CTransPath, PLIP, and BiomedParse, to process and analyze large image datasets
Perform integrative analysis of pathology images with multiomics datasets (e.g. RNAseq, DNA mutations). Integrate and preprocess datasets and software packages from diverse sources to ensure compatibility with AI tools.
Conduct computation in a GPU-enabled environment on cloud platforms.
Model Development & Optimization Assist in fine-tuning and optimizing AI models for tasks such as image segmentation and classification using python/R.
Add new functions to the current pipeline of imaging data analysis.
Document and present model performance metrics to the team.
Biomarker Discovery Apply deep learning/machine learning techniques to predict mutations and/or protein expression and identify potential biomarkers associated with tumor characteristics and treatment outcomes.
Work with mentors and other researchers to validate findings and assess their relevance to cancer biology and therapeutic strategies.
AI Model interpretability Develop interactive tools enabling pathologists to review whole slide images (WSIs) and evaluate integrated AI predictions on the tumor microenvironment (TME).
The tools will leverage diverse vision-language foundation models to enhance model validation and interpretability and bring insights into cancer biology.
Research & Reporting Conduct literature reviews to keep abreast of the latest developments in deep learning, digital pathology and multiomics integration.
Prepare reports and presentations to share findings with both technical and non-technical audiences.