Postdoctoral Fellow, AIML 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 andcutting-edgetechnologies 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 willleverageinnovative design and process development capabilities toexpeditethe delivery of top-tier medicines to patients globally.

What You Will Achieve

In this roleworkingat the interface of viral genomics antigenicity modeling evolutionary forecasting and deep learningyou willdesign implement andvalidateAI-driven models for prospective vaccine strain selection. More specifically you will:

• Developsequence-based deeplearning modelsfor rapidly evolving virus including:

• transformeror language-model-basedarchitecturesfor viral protein sequencesand

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

• Integrate multi-source surveillance immunogenicity and vaccine efficacydatato compute and evaluate prospective coverage scores for candidate vaccine strains.

• Utilize interpretation frameworkstoidentifykey featuresfor virus evolutional advantage related to infectious disease burdenand vaccine antigen design.

• Conduct rigorous retrospective and prospectivebenchmarkingvalidation.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 tofacilitatereproducibility and recognition in peer-reviewed journals and conferences.

MinimumRequirements

• Ph.D. in Computational Biology Bioinformatics Computer ScienceMachineLearning or acloselyrelated field.

• Demonstratedability to independentlydesign andimplement complex ML modelsevidencedby firstauthor publications or equivalent open-source research contributions.

• Stronghands-on experiencewith deep learningfor sequence data including

• transformer or language-model architectures and

• model training validation and benchmarking on large biological datasets

• Proficiencyin Python andmodernMLframeworks (e.g.PyTorch TensorFlow scikit-learn)with experiencemanaging full modelling pipelines andstatistical 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 recommendationmust be providedprior tointerview.

• Willingness to make a minimum2-year commitment.

• Strong communicationand 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.

PreferredQualifications

• 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 SARSCoV2 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