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2024 R&D Computational Sciences Intern

date posted 04/17/2024
contract type Full time
job id R2732044
location Cambridge, Massachusetts

JOB SUMMARY:

The R&D Computational Sciences Intern is a key member of the Digital Data & R&D Computational Sciences group at Sanofi. Interns will be based on Boston and will work with the team leader, Dr. Ziv Bar-Joseph and with other members of the team.

We are a large and diverse group with expertise in Machine Learning and AI applied to computational molecular biology, computational structure biology and computational medicine . As part of the internship you would work on state of the art data that is used by one of the largest pharmaceutical companies  to study and model disease, design new drugs and test these drugs in clinical trials.

Specifically, internships will focus on:

New methods for the analysis and modeling of time series single cell data and for integrating this data with interaction and imaging data New methods for active learning for designing high throughput experiments. New methods for integrating molecular, clinical and real world data to improve the way clinical trials are designed and performed.

DISCOVER YOUR FUTURE

At Sanofi, we believe our culture is our strongest competitive advantage. We are proud to offer our co-ops amazing learning opportunities along with mentorship, trainings, workshops, and networking events. You will be exposed to various team members across the globe and learn many skills within the Communications space.

Sanofi’s University Program opportunities offer exposure to a fast-paced and collaborative environment. Student hires work side-by-side with biopharmaceutical professionals, providing an opportunity to make an impact in the lives of patients around the globe.

This internship may lead to a full-time opportunity upon graduation based on successful performance within the program.

KEY INDIVIDUAL DUTIES AND RESPONSIBILITIES

  • Develop new methods for the analysis and modeling of time series single cell data and for integrating this data with interaction and imaging data
  • Develop new methods for active learning for designing high throughput experiments
  • Develop new methods for integrating molecular, clinical and real-world data to improve the way clinical trials are designed and performed
  • Collaborate with team members in projects

BASIC QUALIFICATIONS

  • Current student in a PhD program in Computational Biology, Bioinformatics, Computer Science, Machine Learning or related area
  • Knowledge and work in biomedical data analysis
  • Experience in the analysis and modeling of at least one of the following: high throughput genomics / biomedical imaging / protein structure / clinical data
  • Publications (including first author publications) in leading computational biology conferences and / or in leading computational biology, bioinformatics, genomics, or multi-disciplinary journals are a plus but not required

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, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status; domestic violence victim status; atypical cellular or blood trait; genetic information (including the refusal to submit to genetic testing) or any other characteristic protected by law.

#GD-SG 
#LI-GZ

At Sanofi diversity and inclusion is foundational to how we operate and embedded in our Core Values. We recognize to truly tap into the richness diversity brings we must lead with inclusion and have a workplace where those differences can thrive and be leveraged to empower the lives of our colleagues, patients and customers. We respect and celebrate the diversity of our people, their backgrounds and experiences and provide equal opportunity for all.

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