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2024 Fall Co-op - Data and Computational Science, mRNA Center of Excellence, Sanofi - Vaccines

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16d ago

🚀 Off-cycle Internship


⌛ Closed
Applications are closed

Off-cycle Internship

Science, EngineeringWaltham


  • Sanofi is dedicated to supporting people through their health challenges. We are a global biopharmaceutical company focused on human health. We prevent illness with vaccines, provide innovative treatments to fight pain and ease suffering. We stand by the few who suffer from rare diseases and the millions with long-term chronic conditions.


  • Basic Qualifications:
  • Currently enrolled in a biology, chemical or biochemical engineering, or equivalent program and working toward BS, MS, or PhD degree program
  • Preferred Qualifications:
  • Basic understanding of mRNA technology
  • Knowledge in enzymatic reactions such as in vitro- transcription, capping and tailing
  • Experience in First principle and hybrid dynamic modeling, and model-based DoE
  • Familiar with mathematical software and packages (Python, Julia, MATLAB, PYOMO, sklearn, gPROMS)

Education requirements

Currently Studying

Area of Responsibilities



  • The Co-op position is based in the Data and Computational Science (DCS) department within the Sanofi mRNA Center of Excellence (mRNA CoE) supporting the activities in the CMC data science group. This position will focus on In Vitro Transcription (IVT) modelling, Model-based Design of Experiments (MBDoE), advanced process control (APC), machine learning and data visualization, supporting the main functions in the CMC data science team. The candidate will have opportunities to learn about mRNA technology, unit operations and to demonstrate the benefits of process modeling and advanced control in a non-GMP lab environment.
  • The successful candidate will work with his/her supervisor to develop dynamic models for In Vitro Transcription (IVT) reactions. The activities include to investigate how process parameter influence product quality attributes and uncertainty quantification, design model-based DoE for model structure selection and parameter optimization, and scalability and robustness analysis of the developed models.


Work type

Full time

Work mode