Genmab is an international biotechnology company dedicated to improving patient lives through innovative antibody therapeutics. The Quantitative Systems Pharmacology (QSP) Modeling Intern will contribute to the development and application of mechanistic models in oncology drug discovery, gaining hands-on experience in computational modeling to support research efforts.
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
Skills
- 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
- 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
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