Postdoctoral Fellow, AI/ML Applications for Vaccine
Pfizer
7 months ago
On-site
Pearl River, NY
Clinical Research and Development
What You Will Achieve
- Design, implement, and validate AI-driven models for prospective vaccine strain selection.
- Develop sequence-based deep learning models for rapidly evolving viruses, including transformer/language-model architectures and graph neural networks to predict time-dependent strain dominance changes.
- Integrate multi-source surveillance, immunogenicity, and vaccine efficacy data to compute/evaluate prospective coverage scores for candidate strains.
- Use interpretation frameworks to identify key features behind virus evolutionary advantage (infectious disease burden and vaccine antigen design).
- Perform rigorous retrospective and prospective benchmarking validation and iterative fine-tuning to improve performance.
- Communicate results to technical and non-technical stakeholders; collaborate with cross-disciplinary internal teams and potentially external partners.
- Publish scientific findings with clear, transparent methods to support reproducibility.
Minimum Requirements
- Ph.D. in Computational Biology, Bioinformatics, Computer Science, Machine Learning, or related field.
- Ability to independently design/implement complex ML models (first-author publications or equivalent open-source contributions).
- Hands-on deep learning for sequence data, including transformer/language-model architectures; training/validation/benchmarking on large biological datasets.
- Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn); experience with full modeling pipelines and statistical modeling (regression, mixed-effects).
- Experience with viral/microbial sequence data (alignment, curation, longitudinal analysis).
- Less than 2 years post-degree experience.
- Two letters of recommendation provided prior to interview.
- Willingness to make a minimum 2-year commitment.
- Strong communication/collaboration in a hybrid environment; strong organization and documentation.
Preferred Qualifications
- Viral evolution modeling; fitness/dominance prediction; time-resolved forecasting.
- Protein language models or MSA-based neural networks.
- Antigenicity data integration; SHAP or similar interpretation frameworks.
- Prior work on influenza, SARS-CoV-2, or other rapidly evolving viruses (immune escape/antigenic drift/vaccine design).
Benefits / Additional Information
- Relocation support available.
- Location: On premise.
- Annual base salary range: $64,600.00–$107,600.00; eligible for 7.5% target bonus under Global Performance Plan.
- Benefits include 401(k) with matching contributions and additional retirement contribution, paid vacation/holidays/personal days, paid caregiver/parental and medical leave, and health coverage (medical, prescription drug, dental, vision).
- Design, implement, and validate AI-driven models for prospective vaccine strain selection.
- Develop sequence-based deep learning models for rapidly evolving viruses, including transformer/language-model architectures and graph neural networks to predict time-dependent strain dominance changes.
- Integrate multi-source surveillance, immunogenicity, and vaccine efficacy data to compute/evaluate prospective coverage scores for candidate strains.
- Use interpretation frameworks to identify key features behind virus evolutionary advantage (infectious disease burden and vaccine antigen design).
- Perform rigorous retrospective and prospective benchmarking validation and iterative fine-tuning to improve performance.
- Communicate results to technical and non-technical stakeholders; collaborate with cross-disciplinary internal teams and potentially external partners.
- Publish scientific findings with clear, transparent methods to support reproducibility.
Minimum Requirements
- Ph.D. in Computational Biology, Bioinformatics, Computer Science, Machine Learning, or related field.
- Ability to independently design/implement complex ML models (first-author publications or equivalent open-source contributions).
- Hands-on deep learning for sequence data, including transformer/language-model architectures; training/validation/benchmarking on large biological datasets.
- Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn); experience with full modeling pipelines and statistical modeling (regression, mixed-effects).
- Experience with viral/microbial sequence data (alignment, curation, longitudinal analysis).
- Less than 2 years post-degree experience.
- Two letters of recommendation provided prior to interview.
- Willingness to make a minimum 2-year commitment.
- Strong communication/collaboration in a hybrid environment; strong organization and documentation.
Preferred Qualifications
- Viral evolution modeling; fitness/dominance prediction; time-resolved forecasting.
- Protein language models or MSA-based neural networks.
- Antigenicity data integration; SHAP or similar interpretation frameworks.
- Prior work on influenza, SARS-CoV-2, or other rapidly evolving viruses (immune escape/antigenic drift/vaccine design).
Benefits / Additional Information
- Relocation support available.
- Location: On premise.
- Annual base salary range: $64,600.00–$107,600.00; eligible for 7.5% target bonus under Global Performance Plan.
- Benefits include 401(k) with matching contributions and additional retirement contribution, paid vacation/holidays/personal days, paid caregiver/parental and medical leave, and health coverage (medical, prescription drug, dental, vision).