Associate Scientist, Post Doc Fellow- Immunology Analytics
About The Position
The Postdoctoral Research Fellow will join a cross-functional translational research team studying cellular and tissue mechanisms underlying chronic immune-mediated inflammatory diseases. The role uses large-scale single-cell and spatial transcriptomics datasets from human tissue cohorts to identify disease-driving cellular programs, tissue microenvironments, and translational biomarkers, including in hidradenitis suppurativa and related disorders. The fellow will develop cellular atlases, characterize spatial tissue niches and cellular interactions, and investigate mechanisms of action and biomarkers. The work integrates computational biology, immunology, spatial genomics, and translational medicine to produce biological insights, disease-mechanism models, biomarker hypotheses, and strategies supporting therapeutic discovery and patient stratification. The position is expected to contribute to high-impact publications and presentations. The fellow will receive interdisciplinary mentorship, opportunities to publish and present research, and experience translating complex multi-omics data into therapeutic insights. The role collaborates across Data, AI and Genome Sciences (DAGS), Immunology Discovery, and Translational Medicine teams.
Requirements
- Must currently hold a Ph.D. or receive a Ph.D. no later than spring 2027.
- Ph.D. in Computational Biology, Bioinformatics, Systems Biology, Genomics, Computer Science, Statistics, or a related discipline.
- Strong programming skills in R and/or Python.
- Experience analyzing large-scale genomics or multi-omics datasets.
- Hands-on experience with GPU computing, HPC clusters, and AWS cloud environments for large-scale single-cell and spatial omics analyses.
- Ability to develop and optimize scalable computational pipelines for scRNA-seq, spatial transcriptomics, and multimodal biological data.
- Demonstrated publication record in computational biology, genomics, immunology, or related fields.
- Strong scientific communication, presentation, and collaboration skills.
Nice To Haves
- Experience with single-cell omics workflows such as Seurat, Scanpy, scVI, CellChat, MultiNicheNet, or related tools.
- Experience with spatial transcriptomics platforms such as Xenium, CosMx, Visium, MERFISH, or related technologies.
- Background in immunology, inflammatory diseases, tissue biology, or translational research.
- Experience in machine learning, network analysis, systems biology, or multimodal data integration.
- Familiarity with cloud computing and high-performance computing environments.
- Experience working with large-scale human cohort datasets.
Responsibilities
- Analyze large-scale single-cell RNA-seq, spatial transcriptomics, and related multi-omics datasets.
- Develop and implement computational methods for data integration, annotation, and interpretation.
- Apply advanced bioinformatics, statistical modeling, machine learning, and network biology approaches.
- Integrate transcriptomic, spatial, proteomic, and clinical data to generate biological insights.
- Collaborate with immunologists, translational scientists, clinicians, computational researchers, and teams across DAGS, Immunology Discovery, and Translational Medicine.
- Lead publication-quality analyses and prepare manuscripts for peer-reviewed journals.
- Present findings at internal scientific forums and external conferences.
- Contribute to innovative analytical methods and reusable computational resources.
- Build cellular atlases by developing single-cell references, identifying and annotating disease-associated cell states, harmonizing populations across cohorts, and characterizing pathways and regulatory programs.
- Analyze spatial transcriptomics to locate disease-associated cell states, characterize cellular neighborhoods and interactions, and identify disease-related tissue microenvironments.
- Integrate spatial and single-cell datasets to generate mechanistic hypotheses about disease biology.
- Evaluate target expression and pathway activity across cellular and spatial contexts.
- Identify biomarkers, disease endotypes, and molecular signatures associated with disease biology, therapeutic response, and patient stratification.
- Integrate multimodal datasets to construct disease-mechanism and translational biology models.
Benefits
- Medical, dental, and vision healthcare benefits.
- Other insurance benefits for employees and family.
- Retirement benefits, including 401(k).
- Paid holidays.
- Vacation.
- Compassionate and sick days.
- Eligibility for annual bonus and long-term incentive, if applicable.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
