Postdoctoral Fellow, AI/ML Applications for Vaccine

Use Your Power for Purpose 

At Pfizer, our purpose is to deliver breakthroughs that transform patients' lives. Central to this mission is our Research and Development team, which strives to convert advanced science and cutting-edge technologies into impactful therapies and vaccines. Whether you are engaged in discovery sciences, ensuring drug safety and efficacy, or supporting clinical trials, your role is crucial. You will leverage innovative design and process development capabilities to expedite the delivery of top-tier medicines to patients globally. 
 

What You Will Achieve 

In this role, working at the interface of viral genomics, antigenicity modeling, evolutionary forecasting, and deep learning, you will design, implement, and validate AI-driven models for prospective vaccine strain selection. More specifically, you will: 

• Develop sequence-based deep learning models for rapidly evolving virus, including:

• transformer or language-model-based architectures for viral protein sequences and  

• graph neural networks that predict time-dependent changes in strain dominance.  

• Integrate multi-source surveillance, immunogenicity, and vaccine efficacy data to compute and evaluate prospective coverage scores for candidate vaccine strains. 

• Utilize interpretation frameworks to identify key features for virus evolutional advantage related to infectious disease burden and vaccine antigen design. 

• Conduct rigorous retrospective and prospective benchmarking validation. iterative fine-tuning to improve model performance 

• Communicate complex data and results clearly to both technical and non-technical stakeholders. Collaborate extensively with those from other scientific disciplines within the group, from other subdivisions of Pfizer, and potentially from external partners. 

• Publish impactful scientific findings while safeguarding confidential data, ensuring clear, transparent reporting of methods and results to facilitate reproducibility and recognition in peer-reviewed journals and conferences. 

Minimum Requirements 

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Machine Learning, or a closely related field. 

• Demonstrated ability to independently design and implement complex ML models, evidenced by first‑author publications or equivalent open-source research contributions. 

• Strong hands-on experience with deep learning for sequence data, including 

• transformer or language-model architectures, and 

• model training, validation, and benchmarking on large