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Fall Post Doctoral Fellow - Process Modeling and CMC Data Science - Vaccines at Sanofi

Job in Waltham, Middlesex County, Massachusetts, 02254, USA
Listing for: Payfuture Technologies
Full Time position
Listed on 2026-05-28
Job specializations:
  • Research/Development
    Data Scientist, Research Scientist
Salary/Wage Range or Industry Benchmark: 95000 USD Yearly USD 95000.00 YEAR
Job Description & How to Apply Below
Position: Fall 2025 Post Doctoral Fellow - Process Modeling and CMC Data Science - Vaccines at Sanofi (US[...]

Fall 2025 Post Doctoral Fellow – Process Modeling and CMC Data Science – Vaccines

Location:

Waltham, MA

About the Job

The Postdoctoral Fellow position is located within the Data and Computational Science (DCS) department at the Sanofi mRNA Center of Excellence (mRNA CoE), supporting the CMC Data Science Group. This role focuses on developing multiscale mechanistic models to investigate the formation and production of lipid nanoparticles (LNPs) used in the delivery of nucleic acid therapeutics. It is a unique opportunity to gain hands‑on experience with mRNA technologies and unit operations, while demonstrating the impact of mechanistic, hybrid, and machine learning models in a non‑GMP laboratory environment.

Sanofi is an innovative global healthcare company that helps the world stay ahead of infectious diseases by delivering more than 500 million vaccine doses a year.

Main Responsibilities
  • Develop multiscale mechanistic, hybrid, or machine learning models for LNP formation and production.
  • Apply these models to characterize and optimize LNP manufacturing processes.
  • Champion and implement model‑based strategies to accelerate process development across Sanofi.
  • Communicate progress through technical presentations and comprehensive reports to cross‑functional stakeholders.
  • Present and publish research findings at scientific conferences and in peer‑reviewed journals.
Basic Qualifications
  • PhD in Chemical Engineering, Biochemical Engineering, or Bioengineering.
  • Experience with and understanding of mRNA unit operations, including in vitro transcription and LNP production.
  • Experience with modeling and data analysis tools such as Python (Scikit‑learn), Julia, MATLAB, PYOMO, and gPROMS.
  • Proven track record of scientific publications and conference presentations.
Preferred Qualifications
  • Publications related to LNPs, particularly those involving computational or modeling approaches.
  • Experience with machine learning and hybrid modeling techniques.
  • Familiarity with numerical algorithms for solving transport equations and population balance models.
  • Excellent communication skills in a collaborative, international setting.
Why Choose Us
  • Bring the miracles of science to life alongside a supportive, future‑focused team.
  • Discover endless opportunities to grow your talent and drive your career, whether it’s through a promotion or lateral move, at home or internationally.
  • Enjoy a thoughtful, well‑crafted rewards package that recognizes your contribution and amplifies your impact.
  • Exposure to cutting‑edge technologies and research methodologies.
  • Networking opportunities within Sanofi and the broader biotech community.

Salary: $95,000 annual. All compensation will be determined commensurate with demonstrated experience.

Employees may be eligible to participate in company employee benefit programs. Additional benefits information can be found through internal resources.

Sanofi Inc. and its U.S. affiliates are Equal Opportunity and affirmative action employers committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, age, ancestry, nationality, marital or domestic partnership status, sex, gender identity or expression, sexual orientation, disability, veteran or military status, domestic violence victim status, atypical cellular or blood trait, genetic information (including refusal to submit to genetic testing), or any other characteristic protected by law.

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