Sanofi Group Post Doctoral Fellow - Computational Modeling and Machine Learning in Cambridge, Massachusetts
Join our dynamic team at Sanofi as a Postdoctoral Research Fellow focused on advancing the field of autonomous experimentation through the development and deployment of cutting-edge machine learning (ML) algorithms. In this role, you'll contribute to a research area that centers on revolutionizing drug development, unleashing the potential of artificial intelligence (AI) to transform process design and the manufacture of synthetic molecules. Specifically, this post-doctoral role is focused on: (1) developing a reaction network generator for a given set of starting materials, reagents, and catalysts, (2) applying active learning and optimization methodologies to optimize knowledge extraction and scale-independent kinetic model development from experiments.
We are seeking an ambitious and innovative researcher who is passionate about pushing the boundaries of both computational modeling and experimental chemistry. If you are excited about contributing to the development of state-of-the-art ML assisted autonomous experimentation tools, we encourage you to apply and be part of our collaborative and forward-thinking research team at Sanofi.
Build an artificial intelligence/machine learning (AI/ML) model capable of generating reaction networks based on existing chemical knowledge. This involves integrating quantum chemistry tools, external databases, and literature data to provide comprehensive lists of possible reactions for specified starting materials, reagents, and catalysts.
Develop and apply filters/rules based on thermodynamics and reaction feasibility to refine and narrow down generated reaction networks.
Implementing and optimizing active learning algorithms to guide the selection of experiments, ensuring efficient data acquisition and model improvement.
Collaborate closely with fellow data scientists and experimentalists to seamlessly integrate the developed tool into an automated AI-assisted kinetic learning algorithm.
Qualifications & Requirements:
Ph.D. (must hold or receive by the time of the start date) in chemical engineering, chemistry, computational/theoretical chemistry, or a related field.
Proficient understanding of synthetic reaction transformations and in-depth expertise in mechanistic analysis.
Expertise in Deep Learning architectures, including Graph Neural Networks (GNNs), active learning, and reinforcement learning.
Strong background in computational chemistry, with a proven track record of applying computational techniques to address complex chemical challenges.
Demonstrated proficiency in programming (Proficient in Python), exemplified by successful completion of university coursework or a compelling portfolio showcasing previous code projects.
Excellent written and oral communication skills
Ability to interact with different interlocutors, in particular development teams (data engineering, ITS and Architecture / Ops teams)
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.
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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