Key Skills

Job Description

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! Applying to multiple Translational Data Science internships? We will be posting several internship projects for this team, each with its own research focus, recruitment team, and selection process. Please take time to explore the available projects and apply to a maximum of two internships that best match your interests and skills. If you apply to two projects, please state your first and second preference in your motivation letter for each application, and briefly explain what attracts you to each. This helps our teams understand your interests and coordinate the separate selection processes! The Role Are you excited about the intersection of AI and cancer immunology, looking to make a meaningful impact in the field of antibody therapeutics? Do you want to gain exposure to the strategic design of innovative antibody therapeutics? Keep reading! We are looking for a highly motivated intern to join Genmab’s Translational Data Science team. You will work on two connected directions: multimodal models that integrate clinical imaging with molecular and genomic profiles, and an interpretable tissue-token codebook that translates morphology across radiology, whole-slide pathology, and molecular programs. The work will use public and commercial data to advance biomarker discovery and biologic hypothesis generation. Responsibilities This internship will pursue two complementary pillars that connect predictive modeling with interpretable, reusable representations of tumor biology: Pillar 1 – Multimodal Biomarker Discovery: Design and validate models that integrate CT, MRI, or PET radiomics with molecular, genomic, and clinical data to identify meaningful, actionable biomarkers. Pillar 2 – Tissue Token Codebook Across Scales: Learn discrete tissue tokens in a shared semantic space spanning CT habitats, whole-slide imaging patterns, and molecular programs. Ground tokens in imaging, pathology, and omics evidence to create an interpretable biologic dictionary rather than a black-box embedding. Data Integration & Method Development: Use HEST-1k, TCGA, CPTAC-linked cohorts, radiogenomic imaging cohorts, and relevant internal resources. Explore CNNs, vision transformers, self-supervised learning, cross-attention, graph methods, and discrete representation learning. Feature Interpretation & Hypothesis Generation: Connect learned representations with genetic alterations, immune profiles, pathway activity, morphology, treatment response, and outcomes to support biomarker discovery and cross-study reuse. Model Validation, Generalization & Reproducibility: Conduct cross-validation, ablation studies, and external validation. Write clean, modular, well-documented code following reproducible research practices. Collaboration & Communication: Work closely with computational biologists, bioinformaticians, pathologists, imaging scientists, and clinicians. Present progress and findings in internal seminars and contribute to internal reports and potential publications. Requirements Currently enrolled in a Master’s degree program at a Dutch university in Computer Science, Artifical Intelligence or a related quantitative discipline. This internship must be a formal, mandatory component of your degree program required for graduation; we cannot consider candidates who have already graduated or who are seeking an extracurricular internship. Available for a minimum of 6 months. Proficiency in Python and deep learning frameworks such as PyTorch. Experience developing CNNs, transformers, self-supervised models, or multimodal architectures for medical imaging, digital pathology, omics, or related applications. Experience with data management, experiment tracking, and scalable training workflows (e.g., Weights & Biases). Familiarity with medical imaging or pathology libraries and workflows (e.g., MONAI, PyRadiomics, OpenSlide) is desirable. Knowledge of representation learning, interpretability, clustering or vector quantization, and biomedical evaluation methods. Strong understanding of or interest in cancer biology, pathology, radiology, or spatial omics is highly desirable. Soft Skills: Analytical mindset, collaborative spirit, strong organizational skills, clear communication, and a proactive attitude. General Information Internship duration: 6-9 months. Start date: February 1st 2027. Location: Utrecht, Netherlands The proposed gross annual/monthly base salary range for this position, in the primary location, based on a full time schedule is: EUR750,00---750,00 The final salary offer will depend on several factors, including your skills, qualifications, and experience. In addition to base salary, this position is eligible for additional forms of compensation, such as discretionary bonuses and long-term incentives. Benefits When you join Genmab, you become a part of a culture that supports your physical, financial, social, and emotional well-being. Our benefits include, but are not limited to: Pension Health insurance and wellness benefits Paid time off Employee support programs Further details on eligibility for compensation and benefits based on the role will be provided during the recruitment process. About Genmab Genmab is an international biotechnology company with a core purpose to improve the lives of patients through innovative and differentiated antibody therapeutics. For 25 years, its hard-working, innovative and collaborative team has invented next-generation antibody technology platforms and harnessed translational, quantitative and data sciences, resulting in a proprietary pipeline including bispecific T-cell engagers, antibody-drug conjugates, next-generation immune checkpoint modulators and effector function-enhanced antibodies. By 2030, Genmab’s vision is to transform the lives of people with cancer and other serious diseases with Knock-Your-Socks-Off (KYSO®) antibody medicines. Established in 1999, Genmab is headquartered in Copenhagen, Denmark with international presence across North America, Europe and Asia Pacific. For more information, please visit Genmab.com and follow us on LinkedIn and X. Genmab is committed to protecting your personal data and privacy. Please see our privacy policy for handling your data in connection with your application on our website Job Applicant Privacy Notice (genmab.com). Please note that if you are applying for a position in the Netherlands, Genmab’s policy for all permanently budgeted hires in NL is initially to offer a fixed-term employment contract for a year, if the employee performs well and if the business conditions do not change, renewal for an indefinite term may be considered after the contract.

Core Responsibilities

Develop and validate multimodal models that combine medical imaging with molecular, genomic, and clinical data to discover actionable biomarkers, and create interpretable tissue-token representations across radiology, pathology, and molecular programs. Integrate and analyze biomedical datasets, validate models for generalization and reproducibility, collaborate with multidisciplinary experts, and communicate findings through presentations, reports, and potential publications.

Requirements

Applicants must be enrolled in a master's program at a Dutch university in computer science, artificial intelligence, or a related quantitative field, with the internship required for graduation, and be available for at least six months. They should have Python and PyTorch proficiency, experience with deep learning and multimodal or biomedical data, and knowledge of data workflows and representation learning; familiarity with medical imaging tools and cancer biology is desirable.

Benefits

• Pension
• Health Insurance
• Wellness Benefits
• Paid Time Off
• Employee Support Programs
• Discretionary Bonuses
• Long-Term Incentives