AI-Driven Radiomics and Multimodal Biomarker Discovery Intern
Genmab is an international biotechnology company dedicated to improving the lives of patients through innovative antibody therapeutics. The AI-Driven Radiomics and Multimodal Biomarker Discovery Intern will work on developing deep learning frameworks for biomarker discovery in cancer treatment, integrating clinical imaging with genomic profiles.
Responsibilities
- Deep Learning Architecture Development: Design, train, and optimize advanced CNN-based and hybrid architectures (e.g., 3D CNNs, Vision Transformers, CNN -Transformer hybrids) to extract biologically meaningful radiomic representations
- Multimodal Data Integration: Develop deep fusion models that combine imaging, molecular, and clinical data through cross-attention, late fusion, or graph-based techniques to enhance biomarker prediction and interpretability
- Software Engineering & Reproducibility: Write clean, modular, and well-documented code following modern software engineering best practices
- Feature Interpretation & Biomarker Discovery: Correlate learned features with genetic mutations (e.g., KRAS, EGFR, TP53), immune profiles, and clinical outcomes to identify interpretable and actionable biomarkers
- Model Validation & Generalization: Conduct rigorous cross-validation, hyperparameter optimization, and external dataset validation to assess model robustness and reproducibility
- Collaboration & Communication: Work closely with computational biologists, bioinformaticians, and clinicians. Present progress and findings in internal seminars and contribute to internal reports and potential publications
Skills
- Currently pursuing a PhD or advanced Master's degree in Computer Science, Data Science, Biomedical Engineering, Computational Biology, or a related quantitative discipline
- Proficiency in Python and deep learning frameworks such as PyTorch
- Demonstrated experience developing CNNs, transformers, or multimodal architectures for medical imaging, omics, or related AI applications
- Experience with data management and distributed training frameworks (e.g., Weights & Biases)
- Familiarity with radiomics extraction libraries (e.g., PyRadiomics, MONAI)
- Knowledge of model interpretability tools (Grad-CAM, SHAP, feature attribution) and evaluation metrics for biomedical data
- Soft Skills: Analytical mindset, collaborative spirit, strong organizational skills, and proactive attitude
- Strong understanding of or interest in cancer biology
Company Overview