Quantitative Systems Pharmacology (QSP) Modeling Intern
About The Position
Genmab’s Quantitative Systems Pharmacology (QSP) team is seeking a motivated and talented PhD-level intern to contribute to the development and application of mechanistic models supporting our oncology drug discovery and development programs. The intern will gain hands-on experience building computational models to understand disease biology, pharmacokinetics/pharmacodynamics (PK/PD), and drug mechanisms of action for antibody-based therapeutics. This position offers the opportunity to work alongside experienced scientists in a collaborative, multidisciplinary environment, applying advanced modeling tools to generate actionable insights that guide experimental design and decision-making across preclinical and clinical stages.
Requirements
- Currently enrolled in a PhD program in engineering, applied mathematics, systems biology, pharmaceutical sciences or related quantitative discipline.
- Strong experience with MATLAB and SimBiology for model development and simulation.
- Solid foundation in differential equations, reaction kinetics, and quantitative biological systems modeling.
- Demonstrated ability to analyze complex datasets and extract mechanistic insights.
- Excellent communication and collaboration skills in multidisciplinary settings.
- Self-motivated with strong attention to detail and problem-solving ability.
Nice To Haves
- Familiarity with pharmacokinetic/pharmacodynamic (PK/PD) modeling concepts and systems pharmacology frameworks.
- Experience modeling biological pathways or cellular dynamics relevant to oncology or immunology.
- Publication or presentation experience in computational modeling, systems biology, or pharmacometrics.
Responsibilities
- Contribute to quantitative systems pharmacology (QSP) projects by developing, adapting, or applying mechanistic models to address key research questions in oncology.
- Perform simulations and analyses under various biological or experimental conditions to explore system behavior and treatment responses.
- Integrate preclinical and literature-derived data to inform model structure, parameterization, and evaluation.
- Visualize and interpret model outputs to generate insights that support hypothesis generation or experimental design.
- Collaborate with scientists across disciplines to ensure alignment between modeling objectives, available data, and project needs.
- Document modeling workflows, assumptions, and findings in clear and reproducible formats, and communicate results effectively to cross-functional teams.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
Number of Employees
1,001-5,000 employees
