Summer Intern: Computational Drug Discovery & MD/ML

Sanofi

Cambridge (MA)

On-site

USD 40,000 - 48,000

Full time

7 days ago
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Job summary

Sanofi in Cambridge, MA invites a Summer Intern to join the US-IDD Medicinal Chemistry team. You will work at the intersection of molecular dynamics, machine learning, and drug design with mentorship from experienced scientists.

This 10–12 week program offers hands-on experience with computational methods and exposure to the role of computational chemistry in drug discovery, supporting real-world healthcare outcomes.

Qualifications

  • Currently pursuing a Bachelor's degree in chemistry, biology, computer science or a related field.
  • Experience with data visualization and basic programming for data analysis.
  • Familiarity with molecular dynamics simulations and related software is a plus.

Responsibilities

  • Contribute to data visualization and interpretation of complex structural and dynamical information.
  • Maintain organized computational workflows and documentation of methodologies and results.
  • Generate and analyze protein structure predictions and conformational ensembles using computational tools.
  • Apply machine learning and statistical methods to analyze simulation data.
  • Support molecular dynamics simulations and conformational sampling to investigate protein structural dynamics.
  • Continue to learn from literature and meetings to solve chemistry challenges.
  • Communicate effectively with team members.

Skills

Molecular dynamics
Data visualization
Programming basics
Machine learning basics

Education

Bachelor's degree in related field

Tools

GROMACS

Job description

Sanofi in Cambridge, MA invites a Summer Intern to join the US-IDD Medicinal Chemistry team. You will work at the intersection of molecular dynamics, machine learning, and drug design with mentorship from experienced scientists.

This 10–12 week program offers hands-on experience with computational methods and exposure to the role of computational chemistry in drug discovery, supporting real-world healthcare outcomes.

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