Postdoctoral Research Fellow - Agentic AI for Spatial Modeling of Disease Microe
Pfizer, S.A. de C.V, San Diego, CA, United States
Postdoctoral Research Fellow - Agentic AI for Spatial Modeling of Disease Microenvironments
We are seeking a highly motivated Postdoctoral Research Fellow to develop next‑generation AI methods for modeling disease microenvironments using spatial omics, digital pathology, and language‑model–based cell representations. This joint role spans Oncology R&D (ORD) and Inflammation & Immunology R&D (I&I) to accelerate cross‑portfolio discovery through integrative computational modeling of tissue architecture, immune engagement, and disease biology.
Role Responsibilities:
Immune activation and suppression may be spatially restricted to defined tissue interface zones.
Disease transcriptional programs may regulate immune cell biodistribution, accessibility, and functional contact probability.
Fibroblast‑ and myeloid‑driven suppressive niches may constrain effector‑cell distribution and function.
Develop and apply methods combining spatial transcriptomics / spatial omics, digital pathology foundation models, cell‑state language models, heterogeneity profiling, and agentic AI workflows for iterative multimodal reasoning.
Develop computational methods for spatial modeling of tumor–immune and pathogenic tissue–immune interactions using multimodal datasets.
Build and evaluate AI/ML workflows that integrate spatial omics, histopathology, and clinical outcome data.
Advance cell‑state representation learning using language‑model–based approaches for single‑cell and spatial biology.
Apply boundary‑resolved profiling methods to quantify immune–disease interactions in spatial contexts.
Fine‑tune digital pathology foundation models for biomarker discovery, reverse translation, and patient stratification.
Contribute to the design of an agentized AI platform for scalable analysis over multimodal biomedical data.
Collaborate with scientists across Oncology R&D and Inflammation & Immunology R&D, including computational, translational, pathology, and biology stakeholders.
Present findings internally and externally, prepare manuscripts, and support new project ideas and translational hypotheses.
Reporting Structure:
Jointly monitored and mentored by investigators from Oncology R&D and Inflammation & Immunology R&D. Operates in a highly collaborative matrix environment and engages with scientific mentors and stakeholders across both therapeutic areas.
Required Qualifications:
PhD in Computational Biology, Bioinformatics, Computer Science, Biomedical Engineering, Systems Biology, Statistics, Machine Learning, or a related quantitative discipline.
Hands‑on experience in one or more of the following areas: spatial transcriptomics and/or single‑cell omics, computational pathology and/or digital pathology, large language models, agentic AI, and their applications in computational biology.
Less than 2 years post‑degree experience.
Willingness to make a minimum 2‑year commitment.
Provide two letters of recommendation.
Demonstrated record of scientific accomplishment, evidenced by peer‑reviewed publications and/or conference presentations, including at least one first‑author publication.
Proficiency in Python and modern scientific computing and machine learning frameworks.
Ability to work independently while collaborating effectively within a multidisciplinary research team.
Strong written and verbal communication skills, with the ability to clearly convey complex scientific concepts.
Preferred Qualifications:
Experience with foundation models, representation learning, or LLM‑inspired approaches in biology.
Experience working 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 modeling in disease biology.
High‑impact publications, conference presentations, and internal translational assets.
Last date to apply: April 30th, 2026.
EEO & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E‑Verify employer. This position requires permanent work authorization in the United States.
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We are seeking a highly motivated Postdoctoral Research Fellow to develop next‑generation AI methods for modeling disease microenvironments using spatial omics, digital pathology, and language‑model–based cell representations. This joint role spans Oncology R&D (ORD) and Inflammation & Immunology R&D (I&I) to accelerate cross‑portfolio discovery through integrative computational modeling of tissue architecture, immune engagement, and disease biology.
Role Responsibilities:
Immune activation and suppression may be spatially restricted to defined tissue interface zones.
Disease transcriptional programs may regulate immune cell biodistribution, accessibility, and functional contact probability.
Fibroblast‑ and myeloid‑driven suppressive niches may constrain effector‑cell distribution and function.
Develop and apply methods combining spatial transcriptomics / spatial omics, digital pathology foundation models, cell‑state language models, heterogeneity profiling, and agentic AI workflows for iterative multimodal reasoning.
Develop computational methods for spatial modeling of tumor–immune and pathogenic tissue–immune interactions using multimodal datasets.
Build and evaluate AI/ML workflows that integrate spatial omics, histopathology, and clinical outcome data.
Advance cell‑state representation learning using language‑model–based approaches for single‑cell and spatial biology.
Apply boundary‑resolved profiling methods to quantify immune–disease interactions in spatial contexts.
Fine‑tune digital pathology foundation models for biomarker discovery, reverse translation, and patient stratification.
Contribute to the design of an agentized AI platform for scalable analysis over multimodal biomedical data.
Collaborate with scientists across Oncology R&D and Inflammation & Immunology R&D, including computational, translational, pathology, and biology stakeholders.
Present findings internally and externally, prepare manuscripts, and support new project ideas and translational hypotheses.
Reporting Structure:
Jointly monitored and mentored by investigators from Oncology R&D and Inflammation & Immunology R&D. Operates in a highly collaborative matrix environment and engages with scientific mentors and stakeholders across both therapeutic areas.
Required Qualifications:
PhD in Computational Biology, Bioinformatics, Computer Science, Biomedical Engineering, Systems Biology, Statistics, Machine Learning, or a related quantitative discipline.
Hands‑on experience in one or more of the following areas: spatial transcriptomics and/or single‑cell omics, computational pathology and/or digital pathology, large language models, agentic AI, and their applications in computational biology.
Less than 2 years post‑degree experience.
Willingness to make a minimum 2‑year commitment.
Provide two letters of recommendation.
Demonstrated record of scientific accomplishment, evidenced by peer‑reviewed publications and/or conference presentations, including at least one first‑author publication.
Proficiency in Python and modern scientific computing and machine learning frameworks.
Ability to work independently while collaborating effectively within a multidisciplinary research team.
Strong written and verbal communication skills, with the ability to clearly convey complex scientific concepts.
Preferred Qualifications:
Experience with foundation models, representation learning, or LLM‑inspired approaches in biology.
Experience working 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 modeling in disease biology.
High‑impact publications, conference presentations, and internal translational assets.
Last date to apply: April 30th, 2026.
EEO & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E‑Verify employer. This position requires permanent work authorization in the United States.
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