Associate Scientist, Post Doc Fellow- Immunology Analytics
About The Position
The Postdoctoral Research Fellow will join a cross-functional translational research team investigating cellular and tissue mechanisms underlying chronic immune-mediated inflammatory diseases. The fellow will analyze large-scale single-cell and spatial transcriptomics data from human tissue cohorts to identify disease-driving cellular programs, tissue microenvironments, biomarkers, and translational mechanisms, with a focus that includes hidradenitis suppurativa and related disorders. The work spans computational biology, immunology, spatial genomics, and translational medicine, turning multi-omics datasets into biological insights, biomarker hypotheses, disease-mechanism models, and translational strategies. The fellow will build cellular atlases of immune-mediated inflammatory diseases, define spatial tissue niches and cellular interactions, and contribute to mechanism-of-action and biomarker discovery. Research includes developing and harmonizing single-cell references, identifying cell states and pathways, analyzing tissue architecture and cellular neighborhoods, integrating spatial and single-cell data, and assessing target expression, pathway activity, disease endotypes, and clinical heterogeneity. The position offers mentorship across computational biology, immunology, translational medicine, and drug discovery; opportunities to publish first-author manuscripts and present at scientific conferences; and experience with single-cell, spatial, and multi-omics technologies and interdisciplinary collaboration. The work is expected to contribute to high-impact publications and foundational knowledge for therapeutic discovery, target evaluation, biomarker identification, patient stratification, and precision medicine.
Requirements
- Must currently hold a PhD or receive a PhD no later than spring 2027.
- PhD 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 analysis 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 with 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 bioinformatics, statistical modeling, machine learning, and network biology approaches.
- Integrate transcriptomic, spatial, proteomic, and clinical data to generate biological insights.
- Build single-cell references and atlases, identify disease-associated cell states, and characterize molecular pathways and regulatory programs.
- Analyze spatial transcriptomics data to localize disease-associated cell states, characterize cellular neighborhoods and interactions, and discover 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 associated with disease biology, therapeutic response, and patient stratification.
- Characterize disease endotypes and molecular signatures associated with clinical heterogeneity.
- Integrate multimodal datasets to construct disease-mechanism and translational biology models.
- Collaborate with immunologists, translational scientists, clinicians, computational researchers, and teams across Data, AI and Genome Sciences (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.
Benefits
- Medical, dental, and vision healthcare and other insurance benefits for employees and family.
- Retirement benefits, including 401(k).
- Paid holidays, vacation, compassionate days, and sick days.
- Eligible for an annual bonus and long-term incentive, if applicable.
- Mentorship from experts across computational biology, immunology, translational medicine, and drug discovery.
- Opportunities to publish first-author manuscripts and present at major scientific conferences.
- Opportunities to develop expertise in single-cell, spatial, and multi-omics technologies and build an interdisciplinary network.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
