Spring Co-op: Quantitative Pharmacology & AI in Pharma

Sanofi

Morristown (NJ)

On-site

USD 79,000 - 83,000

Full time

5 days ago
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Job summary

Sanofi in Morristown, NJ invites a Spring Co-op to join the Quantitative Pharmacology team. You will work on building and applying quantitative models, simulating scenarios, and advancing AI/ML–augmented pharmacometrics workflows under senior mentorship.

The role emphasizes real-world modeling within Translational Medicine, literature review, and development of computational tools. Ideal candidates are PhD students in quantitative fields, eager to learn and contribute across a multidisciplinary

Qualifications

  • PhD candidate in statistics, biostatistics, biomedical engineering, applied mathematics, or related quantitative discipline.
  • Must be permanently authorized to work in the U.S. without visa sponsorship at application time or in the future.
  • Must be enrolled in an accredited college or university for the duration of the co-op/internship.
  • Must relocate to Morristown, NJ and work 40hrs/week for the full duration.

Responsibilities

  • Develop and apply quantitative, mathematical, and statistical models and simulations with experienced scientists.
  • Explore AI/ML opportunities to augment pharmacometrics workflows within a quantitative framework.
  • Conduct literature reviews and build computational tools for model-informed decision support.

Skills

PK/PD
Pharmacometry
Statistics
Machine learning
Data modeling
Data visualization
Python
R
AI/ML tools

Education

PhD candidate in statistics/biostatistics/biomedical engineering/applied mathematics

Tools

Python
R
Matlab

Job description

Sanofi in Morristown, NJ invites a Spring Co-op to join the Quantitative Pharmacology team. You will work on building and applying quantitative models, simulating scenarios, and advancing AI/ML–augmented pharmacometrics workflows under senior mentorship.

The role emphasizes real-world modeling within Translational Medicine, literature review, and development of computational tools. Ideal candidates are PhD students in quantitative fields, eager to learn and contribute across a multidisciplinary

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