Postdoctoral Fellow in AI​/ML Vaccine Research & Development

Postdoctoral Fellow in AI/ML Applications for Vaccine Research & Development

We’re in relentless pursuit of breakthroughs that change patients’ lives. We innovate every day to make the world a healthier place.

To fully realize Pfizer’s purpose – Breakthroughs that change patients’ lives – we have established a clear set of expectations regarding “what” we need to achieve for patients and “how” we will go about achieving those goals.

Pfizer Research & Development serves as the beating heart of Pfizer's trailblazing product pipeline, the essence of our mission to bring life‑changing medicines to the world.

Pfizer offers competitive compensation and benefits programs designed to meet the diverse needs of our colleagues.

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.

• Develop advanced AI and machine learning models, including transformer architectures and graph neural networks, to represent molecular features and predict immunogenicity of pneumococcal conjugate vaccines using preclinical and clinical data.
• Apply transfer learning to translate predictive models from preclinical to clinical domains and utilize interpretation frameworks (such as SHAP) to identify key molecular motifs for rational vaccine design.
• Conduct meta‑analyses of large‑scale immunogenicity datasets to characterize quantitative relationships between vaccine physical parameters and immunogenicity outcomes.
• Harmonize and curate preclinical and clinical datasets to support robust statistical and machine learning analyses.
• Engage in active collaboration with multidisciplinary teams, encompassing experts in data science, preclinical analysis, and clinical vaccine research, to advance pneumococcal conjugate vaccines development.
• 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.

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Immunology, or a related field.
• Successful record of scientific accomplishments evidenced by scientific publications and/or presentations with at least one first‑author publication in a peer‑reviewed journal.
• No more than 2 years of post‑degree experience.
• Willingness to make a minimum 2‑year commitment.
• Demonstrated expertise in machine learning and deep learning, with hands‑on experience in developing and validating predictive models for biological or biomedical data.
• Proficiency in Python and relevant AI machine learning frameworks (e.g., PyTorch, Tensor Flow, scikit‑learn). Experience with statistical modeling, including regression analysis and mixed‑effects models.
• Solid understanding of immunology, especially vaccine immunogenicity and conjugate vaccine design.
• Strong data management and curation skills, including harmonization of heterogeneous biological datasets.
• Excellent scientific communication skills, with a track record of peer‑reviewed publications or presentations. Ability to work across computational and wet‑lab teams, distill complex results for diverse stakeholders.

• Prior experience applying graph neural networks, transformer models, or cross‑attention mechanisms to biological sequence or molecular structure data.
• Familiarity with glycan‑focused modeling and representation, including encoding of polysaccharide and protein carrier features.
• Experience in translational research bridging preclinical and clinical datasets, especially in vaccine development.
• Knowledge of SHAP or similar model interpretation frameworks for feature attribution…