Co-op, Clinical Pharmacology & Pharmacometrics

Biogen

1d•$29 - $33

About The Position

This is a six-month student co-op role running from January through June 2027 within Biogen’s Clinical Pharmacology & Pharmacometrics (CPP) organization. The role is on the Pharmacometrics team and focuses on quantitative systems pharmacology (QSP), artificial intelligence, and model-based meta-analysis (MBMA) in the autoimmune therapeutic area. The co-op will contribute to mechanistic QSP model development, explore AI and machine-learning methods for improving modeling workflows, and perform MBMA to produce quantitative evidence for drug-development decisions and portfolio strategy. The role offers hands-on exposure to modeling approaches used in industry drug development and collaboration across research, clinical development, regulatory, and other functions. The co-op will analyze and integrate preclinical and clinical data, learn industry-standard data-handling and modeling practices, and communicate work through internal presentations and, when applicable, external posters and manuscripts. Students must be legally authorized to work in the U.S., be at least 18 before the scheduled start date, and be enrolled in an accredited community college, college, university, or skills program/apprenticeship. The primary education requirement is a third-year-or-above Ph.D. candidate with quantitative, computational, or mechanistic modeling experience. Candidates with a master’s degree or Pharm.D. and demonstrated modeling, data-analysis, or computational-biology experience will also be considered.

Requirements

Strong interest in QSP, mechanistic modeling, and model-informed drug development.
Experience developing or applying mathematical, computational, or systems-biology models to address complex biological questions.
Interest in applying AI/ML methods to enhance modeling workflows, data integration, and scientific decision-making.
Proficiency in R, Python, MATLAB, or similar scientific computing tools.
Experience analyzing and interpreting biological, preclinical, clinical, omics, or literature-derived datasets.
Familiarity with meta-analysis, model-based analysis, evidence synthesis, or related quantitative methods.
Strong quantitative reasoning, scientific problem-solving, and critical-thinking skills.
Ability to independently learn and apply new computational, analytical, and modeling techniques.
Strong written and verbal communication skills, including the ability to present complex scientific concepts clearly.
Ability to work effectively in a collaborative, multidisciplinary research environment.
Legal authorization to work in the U.S.
At least 18 years of age before the scheduled start date.
Currently enrolled in an accredited community college, college, university, or skills program/apprenticeship.
Ph.D. candidate in the third year or above with experience in quantitative, computational, or mechanistic modeling; candidates with a master’s degree or Pharm.D. and demonstrated modeling, data-analysis, or computational-biology experience will also be considered.

Nice To Haves

Study in Quantitative Systems Pharmacology (QSP), Pharmaceutical Sciences, Clinical Pharmacology/Pharmacokinetics, Systems Biology or Computational Biology, Biomedical Engineering, Biostatistics or Applied Mathematics, Data Science, or a related quantitative life-science discipline.

Responsibilities

Contribute to the development and refinement of mechanistic QSP models supporting drug discovery and development programs.
Explore and evaluate AI and machine-learning approaches to enhance and accelerate QSP modeling workflows.
Perform MBMA to generate quantitative evidence for portfolio strategy and drug-development decisions.
Analyze, integrate, and interpret preclinical and clinical data to support model development and decision-making.
Learn and implement industry-standard data-handling and modeling rules.
Prepare internal presentations on work progress on an ongoing basis.
Prepare external posters and manuscript publications when applicable.
Support additional CPP team tasks as needed.