Postdoctoral Fellow, AI/ML & Computational Immunology
Pfizer, S.A. de C.V, Cambridge, MA, United States
Postdoctoral Fellow, AI/ML & Computational Immunology
Location: United States - Massachusetts - Cambridge
About the Role
The Systems Immunology group within the Inflammation and Immunology Research Unit is seeking a highly motivated Ph.D.-level computational scientist to join as a Postdoctoral Research Fellow. The role focuses on developing and deploying next-generation AI/ML toolkits to decode macrophage biology, efferocytosis, and myeloid cell states in inflammation and fibrotic disease pathophysiology.
Responsibilities
Design and build machine learning models to detect, deconvolve, and interpret efferocytosis events from single‑cell and spatial omics data.
Generate and curate training/validation datasets, including cross‑species and multi‑omic settings, and establish robust benchmarking and quality metrics.
Develop an end‑to‑end analysis toolkit or pipeline (data ingestion, model training, inference, interpretability, and reporting) with reproducible workflows.
Integrate model outputs with downstream biological questions—linking efferocytosis to macrophage state transitions, pathway activation, and disease‑associated phenotypes.
Apply interpretable machine learning approaches to nominate and prioritize therapeutic targets and hypotheses for experimental follow‑up.
Collaborate closely with wet‑lab and translational partners (myeloid biology, fibrosis, spatial omics teams) to refine biological questions, validate findings, and iterate on models.
Independently interpret and analyze data to communicate findings to mentors and collaborators, making decisions using technical and biological knowledge under supervision.
Communicate results through internal presentations, external conferences, and manuscripts for peer‑reviewed publication.
Ensure all tasks and responsibilities are carried out according to scientific and ethical standards.
Build team skills and foster a culture of scientific excellence and continuous learning.
Qualifications
Ph.D. (or thesis defense within 2–3 months) in computational biology, bioinformatics, computer science, statistics, biomedical engineering, immunology with a strong quantitative focus, or a related field.
Willingness to make a minimum 2‑year commitment.
Less than 2 years post‑doctoral experience.
Provide two letters of recommendation.
At least one first‑author scientific research article in a high‑quality specialty or general‑readership journal.
Experience analyzing single‑cell RNA‑seq data; familiarity with common preprocessing, QC, integration, and annotation approaches.
Proficiency in at least one scientific programming language (Python and/or R) and ability to write maintainable, well‑tested code.
Experience with machine learning methods (e.g., representation learning, deep learning, classification/regression) and model evaluation/benchmarking.
Ability to work with large datasets and modern compute environments (HPC and/or cloud), including reproducible workflow practices.
Strong scientific communication skills (writing and presentations) and the ability to collaborate effectively across disciplines.
Excellent organizational skills, self‑motivated, team‑oriented, and able to multitask with attention to detail.
Ability to work collaboratively in a team environment.
Preferred Qualifications
Experience with spatial transcriptomics/omics (e.g., Visium, VisiumHD, Xenium, Slide‑seq) and spatial analysis frameworks.
Experience with deep learning architectures relevant to omics (e.g., transformers, VAEs, graph neural networks) and/or multi‑task learning.
Familiarity with interpretability techniques (e.g., feature attribution, perturbation/what‑if analyses) and/or causal inference concepts for biological discovery.
Domain knowledge of inflammation and fibrosis biology.
Experience integrating multi‑modal datasets (e.g., RNA+ATAC multiome, protein, imaging) and building reusable toolkits.
Comfort using modern AI assistants (e.g., Claude/Microsoft Copilot) to accelerate coding, documentation, and problem solving—paired with strong scientific judgment and validation.
Proficiency in omic data analysis software and tools (e.g., Seurat, Scanpy, Squidpy, pathway enrichment, network analysis).
In‑depth, hands‑on knowledge of inflammation and fibrosis biology.
Experience or familiarity with wet‑lab protocols for generating high‑dimensional single‑cell RNA‑seq data (e.g., 10X Genomics library preparation, Smart‑Seq2‑4 plate‑based protocols, etc.).
Physical/Mental Requirements
Lifting, sitting, standing, walking, bending, performing mathematical calculations, and performing complex data analysis.
Additional Information
Work location assignment: Flexible. Assigned to a Pfizer site within commuting distance (Cambridge, MA – KSQ) where the employee works three days weekly on site and has the option to work off‑site regularly when appropriate.
Relocation support available.
Equal Opportunity Employer Statement
Pfizer is an equal‑opportunity employer. All qualified applicants, including women, people of color, and individuals with disabilities, are encouraged to apply. This position requires permanent work authorization in the United States.
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Location: United States - Massachusetts - Cambridge
About the Role
The Systems Immunology group within the Inflammation and Immunology Research Unit is seeking a highly motivated Ph.D.-level computational scientist to join as a Postdoctoral Research Fellow. The role focuses on developing and deploying next-generation AI/ML toolkits to decode macrophage biology, efferocytosis, and myeloid cell states in inflammation and fibrotic disease pathophysiology.
Responsibilities
Design and build machine learning models to detect, deconvolve, and interpret efferocytosis events from single‑cell and spatial omics data.
Generate and curate training/validation datasets, including cross‑species and multi‑omic settings, and establish robust benchmarking and quality metrics.
Develop an end‑to‑end analysis toolkit or pipeline (data ingestion, model training, inference, interpretability, and reporting) with reproducible workflows.
Integrate model outputs with downstream biological questions—linking efferocytosis to macrophage state transitions, pathway activation, and disease‑associated phenotypes.
Apply interpretable machine learning approaches to nominate and prioritize therapeutic targets and hypotheses for experimental follow‑up.
Collaborate closely with wet‑lab and translational partners (myeloid biology, fibrosis, spatial omics teams) to refine biological questions, validate findings, and iterate on models.
Independently interpret and analyze data to communicate findings to mentors and collaborators, making decisions using technical and biological knowledge under supervision.
Communicate results through internal presentations, external conferences, and manuscripts for peer‑reviewed publication.
Ensure all tasks and responsibilities are carried out according to scientific and ethical standards.
Build team skills and foster a culture of scientific excellence and continuous learning.
Qualifications
Ph.D. (or thesis defense within 2–3 months) in computational biology, bioinformatics, computer science, statistics, biomedical engineering, immunology with a strong quantitative focus, or a related field.
Willingness to make a minimum 2‑year commitment.
Less than 2 years post‑doctoral experience.
Provide two letters of recommendation.
At least one first‑author scientific research article in a high‑quality specialty or general‑readership journal.
Experience analyzing single‑cell RNA‑seq data; familiarity with common preprocessing, QC, integration, and annotation approaches.
Proficiency in at least one scientific programming language (Python and/or R) and ability to write maintainable, well‑tested code.
Experience with machine learning methods (e.g., representation learning, deep learning, classification/regression) and model evaluation/benchmarking.
Ability to work with large datasets and modern compute environments (HPC and/or cloud), including reproducible workflow practices.
Strong scientific communication skills (writing and presentations) and the ability to collaborate effectively across disciplines.
Excellent organizational skills, self‑motivated, team‑oriented, and able to multitask with attention to detail.
Ability to work collaboratively in a team environment.
Preferred Qualifications
Experience with spatial transcriptomics/omics (e.g., Visium, VisiumHD, Xenium, Slide‑seq) and spatial analysis frameworks.
Experience with deep learning architectures relevant to omics (e.g., transformers, VAEs, graph neural networks) and/or multi‑task learning.
Familiarity with interpretability techniques (e.g., feature attribution, perturbation/what‑if analyses) and/or causal inference concepts for biological discovery.
Domain knowledge of inflammation and fibrosis biology.
Experience integrating multi‑modal datasets (e.g., RNA+ATAC multiome, protein, imaging) and building reusable toolkits.
Comfort using modern AI assistants (e.g., Claude/Microsoft Copilot) to accelerate coding, documentation, and problem solving—paired with strong scientific judgment and validation.
Proficiency in omic data analysis software and tools (e.g., Seurat, Scanpy, Squidpy, pathway enrichment, network analysis).
In‑depth, hands‑on knowledge of inflammation and fibrosis biology.
Experience or familiarity with wet‑lab protocols for generating high‑dimensional single‑cell RNA‑seq data (e.g., 10X Genomics library preparation, Smart‑Seq2‑4 plate‑based protocols, etc.).
Physical/Mental Requirements
Lifting, sitting, standing, walking, bending, performing mathematical calculations, and performing complex data analysis.
Additional Information
Work location assignment: Flexible. Assigned to a Pfizer site within commuting distance (Cambridge, MA – KSQ) where the employee works three days weekly on site and has the option to work off‑site regularly when appropriate.
Relocation support available.
Equal Opportunity Employer Statement
Pfizer is an equal‑opportunity employer. All qualified applicants, including women, people of color, and individuals with disabilities, are encouraged to apply. This position requires permanent work authorization in the United States.
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