Intern, Discovery Data Science (Project: in Silico Biology)

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!

 
Why Genmab 

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. 

 
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 on reading! 
 
We are looking for a highly motivated intern to join the Lead Generation and Screening Data Science team in Utrecht. As part of this internship, you will contribute to the development of in silico datasets and simulation frameworks that underpin data-driven discovery of next-generation antibody therapeutics. The work will involve constructing realistic, assumption-based datasets derived from experimental paradigms such as multifactorial cell-kill assays and Titeseq-like sequencing readouts, enabling systematic exploration of biological and biophysical relationships. These resources will support hypothesis testing, guide experimental design, and provide a basis for predictive modeling of antibody function. You will collaborate closely with scientists, data analysts, and modelers across departments to define relevant parameters and encode them into computational models that can guide experimental strategy and data acquisition efforts. 

A project outline is already set up, yet there is room for you to shape the internship in a way that fits your interests as well as your school’s requirements.
 

Responsibilities

• Design and implement in silico datasets and simulation workflows reflecting key experimental or biological hypotheses. 

• Explore how synthetic data can inform design-of-experiments (DoE), model-based analyses, and quantitative modeling approaches. 

• Develop a modular, well-documented framework for dataset generation and reproducibility. 

• Collaborate with subject-matter experts to formalize assumptions into computationally testable scenarios. 

• Communicate findings effectively to interdisciplinary project teams. 
   
    

Requirements 

• Actively enrolled in a Master’s degree program at a Dutch university, or in an HBO-level Bachelor’s program in Bioinformatics, Biomedical Sciences, Computer Sciences, or a related field. Basic understanding of biological concepts is a must!

• Available for a minimum of 7 months.

• Strong interest in data-driven research, simulation, and experimental design. 

• Proficiency in Python (preferred) or R, and familiarity with data analysis and machine learning. 

• Effective (English) communication skills and comfort collaborating across scientific disciplines. 

• A creative and analytical mindset, with an eagerness