AI-Driven Radiomics & Multimodal Biomarker Discovery Intern | Princeton, NJ
Job ID: R14686
Category: Research & Discovery
Location: Princeton, New Jersey, United States
Job Type: Internship (June – August 2026, Hybrid)
About Genmab
Genmab is a global biotechnology leader committed to improving patient outcomes through innovative antibody therapeutics. With over 25 years of experience, Genmab has pioneered next-generation antibody platforms, including bispecific T-cell engagers, antibody-drug conjugates, and immune checkpoint modulators.
Headquartered in Copenhagen, Denmark, Genmab operates across North America, Europe, and Asia-Pacific, shaping the future of oncology with Knock-Your-Socks-Off (KYSO®) antibody medicines.
Discover more at www.genmab.com.
Internship Overview
The AI-Driven Radiomics & Multimodal Biomarker Discovery Internship offers a 10-week summer program within Genmab’s Translational Data Science team. Interns will work at the cutting edge of computational oncology, integrating radiomic features from clinical imaging with molecular and genomic data to identify predictive biomarkers for precision oncology.
This hybrid internship is based in Princeton, NJ, with 3 days onsite and 2 days remote per week.
Key Responsibilities
AI & Deep Learning Development
Design, train, and optimize advanced CNN-based and hybrid architectures (3D CNNs, Vision Transformers, CNN-Transformer hybrids) for radiomic feature extraction.
Develop multimodal fusion models integrating imaging, molecular, and clinical datasets to enhance biomarker discovery.
Apply self-supervised learning, cross-attention, and graph-based techniques for predictive modeling.
Data Integration & Biomarker Discovery
Correlate learned features with genetic mutations (KRAS, EGFR, TP53), immune profiles, and clinical outcomes.
Conduct rigorous cross-validation, hyperparameter tuning, and external dataset validation to ensure reproducibility.
Software Engineering & Documentation
Write clean, modular, and reproducible code following best practices.
Document workflows for internal reporting and potential publications.
Collaboration & Communication
Collaborate with computational biologists, bioinformaticians, and clinicians.
Present findings in internal seminars and contribute to team discussions on innovative approaches.
Required Qualifications
Pursuing a PhD or advanced Master’s in Computer Science, Data Science, Biomedical Engineering, Computational Biology, or a related quantitative discipline.
Proficient in Python and deep learning frameworks such as PyTorch.
Experience with CNNs, transformers, or multimodal architectures for medical imaging or omics data.
Knowledge of radiomics libraries (e.g., PyRadiomics, MONAI) and model interpretability tools (Grad-CAM, SHAP).
Analytical mindset, collaborative spirit, strong organizational skills, and proactive attitude.
Preferred Qualifications
Strong understanding of or interest in cancer biology.
Experience with distributed training frameworks (e.g., Weights & Biases).
Familiarity with clinical imaging datasets and biomedical evaluation metrics.
Internship Benefits
Gain hands-on experience in AI-driven biomarker discovery and multimodal data integration.
Exposure to cutting-edge computational oncology and translational data science.
Mentorship from leading experts in bioinformatics, data science, and cancer research.
Contribute to real-world projects advancing precision oncology.
About You
Passionate about AI, biomedical data science, and oncology.
Collaborative, innovative, and proactive in fast-paced, dynamic environments.
Committed to quality, reproducibility, and continuous learning.
Gujarat :
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Appleton | Kenosha | Pleasant Prairie | Portage | Waukesha |United states :
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Queensland |Melbourne :
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Kyiv |Lima Region :
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Prague |Chile :
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