Translational Imaging & Multi-Omics Data Science Intern

Genmab•Plainsboro Township, NJ

32d•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 seeking a motivated and curious intern to join the Translational Data Science team. This project focuses on integrating features extracted from digital images with multi-omics data using graph-based modeling to uncover biomarker-driven therapeutic opportunities. The intern will contribute to developing and applying advanced analytics frameworks that connect imaging features with molecular and clinical data to identify subgroups with distinct biological and therapeutic characteristics. This role offers exposure to deep learning, graph neural networks (GNNs), and large-scale multi-modal datasets, fostering collaboration across computational, translational, and clinical functions.

Requirements

Current graduate or undergraduate student in Computer Science, Bioinformatics, Data Science, Computational Biology, or related field.
Proficiency in Python or R; experience with machine learning libraries (e.g., PyTorch, TensorFlow).
Familiarity with image analysis and omics data processing.
Strong analytical and problem-solving skills, with attention to detail.
Interest in translational and precision medicine research.

Nice To Haves

Experience with graph-based learning or network biology approaches.
Background in digital pathology, image analysis, or biomedical data integration.
Familiarity with cloud-based analytical environments (Databricks, AWS).
Excellent communication skills and ability to work in a collaborative team.

Responsibilities

Process and curate histopathology and/or radiomics image datasets and multi-omics profiles from public and internal sources.
Extract quantitative image features using deep learning-based models (e.g., foundation models for whole-slide analysis).
Integrate imaging-derived metrics with molecular and clinical data for downstream analytics.
Build and evaluate graph-based models linking patients and molecular/imaging features.
Apply GNNs and related architectures to explore subtype stratification and biomarker discovery.
Utilize cloud computing platforms (e.g., Databricks, AWS) for scalable computation.
Identify molecular and phenotypic patterns associated with distinct subgroups.
Conduct pathway and gene set enrichment analyses to characterize biological differences.
Explore potential therapeutic hypotheses using public drug response and perturbation datasets.
Develop interpretable visual and computational frameworks that highlight molecular–morphological relationships.
Summarize findings for scientific and strategic discussions through figures, reports, and presentations.