Intern, Translational Data Science (Project: Multiscale AI for Tissue Representation and Biomarker Discovery)
Genmab · Utrecht, Netherlands
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Role summary
Genmab seeks a Master's student intern to develop multimodal AI models integrating clinical imaging, molecular, and genomic data for biomarker discovery in cancer immunology. The role combines deep learning with interpretable tissue representation across radiology, pathology, and molecular data.
What they're looking for
- Currently enrolled in Master's program in Computer Science, AI, or related quantitative discipline at Dutch university
- Internship must be mandatory degree requirement; not extracurricular or post-graduation
- Available minimum 6 months; start February 1, 2027
- Proficiency in Python and deep learning frameworks, particularly PyTorch
- Experience developing CNNs, transformers, self-supervised or multimodal models for medical imaging or digital pathology
- Familiarity with experiment tracking, data management, and scalable training workflows
- Knowledge of representation learning, interpretability methods, or biomedical evaluation approaches
What you'll be doing
- Design and validate multimodal models integrating radiomics with molecular, genomic, and clinical data for biomarker identification
- Develop discrete tissue token codebook spanning CT, whole-slide imaging, and molecular programs in shared semantic space
- Integrate public and commercial datasets using CNNs, vision transformers, self-supervised learning, and graph methods
- Interpret learned representations and connect findings to genetic alterations, immune profiles, and treatment outcomes
- Conduct cross-validation, ablation studies, and external validation; collaborate with computational biologists and clinicians on seminars and reports
Privacy & handoff
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