Postdoctoral Research Fellow - Agentic AI for Spatial Modeling of Disease Microenvironments
Pfizer
12 hours ago
Remote friendly (San Diego, CA)
United States
Clinical Research and Development
Role Responsibilities:
- Develop next-generation AI methods for modeling disease microenvironments using spatial omics, digital pathology, and language-model–based cell representations.
- Help build an agentic AI platform integrating cell-state language models, boundary-resolved spatial profiling, and digital pathology foundation models to generate interpretable, mechanistically grounded insights from multimodal tissue data.
- Develop and apply methods combining: spatial transcriptomics/spatial omics; digital pathology foundation models; cell-state language models (e.g., scGPT, cell2sentence); heterogeneity profiling; agentic AI workflows for iterative multimodal reasoning and analysis.
- Develop computational methods for spatial modeling of tumor–immune and pathogenic tissue–immune interactions using multimodal datasets.
- Build and evaluate AI/ML workflows integrating spatial omics, histopathology, and clinical outcome data.
- Advance cell-state representation learning for single-cell and spatial biology using language-model–based approaches.
- Apply and extend boundary-resolved profiling to quantify immune–disease interactions in spatial contexts.
- Fine-tune and adapt digital pathology foundation models using internal histopathology datasets for biomarker discovery, reverse translation, and patient stratification.
- Contribute to design of an agentized AI platform for scalable analysis and reasoning over multimodal biomedical data.
- Collaborate with scientists across Oncology R&D and Inflammation & Immunology R&D (computational, translational, pathology, biology stakeholders).
- Present findings, prepare manuscripts, and support generation of new project ideas and translational hypotheses.
Required Qualifications:
- PhD in Computational Biology, Bioinformatics, Computer Science, Biomedical Engineering, Systems Biology, Statistics, Machine Learning, or related quantitative discipline.
- Hands-on experience in one or more: spatial transcriptomics/single-cell omics; computational pathology/digital pathology; Large Language Models/Agentic AI and applications to computational biology.
- Less than 2 years post-degree experience.
- Willingness to commit at least 2 years.
- Provide two letters of recommendation.
- Scientific accomplishment with peer-reviewed publications and/or conference presentations, including at least one first-author publication.
- Proficiency in Python and modern scientific computing/ML frameworks.
- Ability to work independently and collaborate within a multidisciplinary team.
- Strong written and verbal communication skills.
Preferred Qualifications:
- Experience with foundation models, representation learning, or LLM-inspired approaches in biology.
- Experience with whole-slide imaging, histopathology, or tissue image analysis.
- Familiarity with spatial statistics, graph-based modeling, or boundary/interface analysis in tissue biology.
- Experience integrating molecular, imaging, and clinical datasets.
- Background in oncology, immunology, inflammation biology, or translational biomarker research.
- Interest in interpretable AI and mechanistic disease modeling.
Benefits (if applicable/explicitly stated):
- Relocation is available.
- Annual base salary range: $64,600.00 to $107,600.00; eligible for Pfizer’s Global Performance Plan with bonus target 7.5% of base salary.
- 401(k) with matching contributions and additional retirement savings contribution; paid vacation/holidays/personal days; paid caregiver/parental and medical leave; health benefits (medical, prescription drug, dental, vision).
Application Instructions:
- Last date to apply: April 30th, 2026.
- Develop next-generation AI methods for modeling disease microenvironments using spatial omics, digital pathology, and language-model–based cell representations.
- Help build an agentic AI platform integrating cell-state language models, boundary-resolved spatial profiling, and digital pathology foundation models to generate interpretable, mechanistically grounded insights from multimodal tissue data.
- Develop and apply methods combining: spatial transcriptomics/spatial omics; digital pathology foundation models; cell-state language models (e.g., scGPT, cell2sentence); heterogeneity profiling; agentic AI workflows for iterative multimodal reasoning and analysis.
- Develop computational methods for spatial modeling of tumor–immune and pathogenic tissue–immune interactions using multimodal datasets.
- Build and evaluate AI/ML workflows integrating spatial omics, histopathology, and clinical outcome data.
- Advance cell-state representation learning for single-cell and spatial biology using language-model–based approaches.
- Apply and extend boundary-resolved profiling to quantify immune–disease interactions in spatial contexts.
- Fine-tune and adapt digital pathology foundation models using internal histopathology datasets for biomarker discovery, reverse translation, and patient stratification.
- Contribute to design of an agentized AI platform for scalable analysis and reasoning over multimodal biomedical data.
- Collaborate with scientists across Oncology R&D and Inflammation & Immunology R&D (computational, translational, pathology, biology stakeholders).
- Present findings, prepare manuscripts, and support generation of new project ideas and translational hypotheses.
Required Qualifications:
- PhD in Computational Biology, Bioinformatics, Computer Science, Biomedical Engineering, Systems Biology, Statistics, Machine Learning, or related quantitative discipline.
- Hands-on experience in one or more: spatial transcriptomics/single-cell omics; computational pathology/digital pathology; Large Language Models/Agentic AI and applications to computational biology.
- Less than 2 years post-degree experience.
- Willingness to commit at least 2 years.
- Provide two letters of recommendation.
- Scientific accomplishment with peer-reviewed publications and/or conference presentations, including at least one first-author publication.
- Proficiency in Python and modern scientific computing/ML frameworks.
- Ability to work independently and collaborate within a multidisciplinary team.
- Strong written and verbal communication skills.
Preferred Qualifications:
- Experience with foundation models, representation learning, or LLM-inspired approaches in biology.
- Experience with whole-slide imaging, histopathology, or tissue image analysis.
- Familiarity with spatial statistics, graph-based modeling, or boundary/interface analysis in tissue biology.
- Experience integrating molecular, imaging, and clinical datasets.
- Background in oncology, immunology, inflammation biology, or translational biomarker research.
- Interest in interpretable AI and mechanistic disease modeling.
Benefits (if applicable/explicitly stated):
- Relocation is available.
- Annual base salary range: $64,600.00 to $107,600.00; eligible for Pfizer’s Global Performance Plan with bonus target 7.5% of base salary.
- 401(k) with matching contributions and additional retirement savings contribution; paid vacation/holidays/personal days; paid caregiver/parental and medical leave; health benefits (medical, prescription drug, dental, vision).
Application Instructions:
- Last date to apply: April 30th, 2026.