Postdoctoral Fellow, AI/ML Applications for Vaccine

Requirements

• Direct experience with viral evolution modeling, fitness/dominance prediction, or time-resolved sequence forecasting.
• Experience building or extending protein language models or MSA-based neural networks for biological inference.
• Familiarity with antigenicity data or related experimental measurements, and how such data can be integrated into machine learning models
• Knowledge of SHAP or similar model interpretation frameworks for feature attribution in complex models.
• Prior work on influenza, SARS ‑ CoV ‑ 2, or other rapidly evolving viruses, particularly in the context of immune escape, antigenic drift, or vaccine design.
• Additional Information
• Relocation support available
• Location: On premise
• Last date to apply June 28, 2026

Benefits

Additional Information

Last date to apply June 28, 2026 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, work ing 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 feature s 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 partner s. 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, M achine L earning , 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 biological datasets Proficiency in Python and modern ML frameworks (e.g., PyTorch , TensorFlow, scikit-learn) , with e xperience managing full modelling pipelines and statistical modeling, including regression analysis and mixed-effects models. Experience working with viral or microbial sequence data, including alignment, curation, and longitudinal analysis across time. Less than 2 years of post-degree experience. Two letters of recommendation must be provided prior to interview . Willingness to make a minimum 2 -year commitment. Strong communication and collaboration skills with the ability to work effectively in a hybrid team environment. Strong organizational skills and attention to detail in managing deadlines and documentation. Ability to clearly communicate complex modeling concepts and results to both technical and biological audiences.

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