Associate Scientist, Post Doc Fellow- Computational Immunology

Job Description Postdoctoral Research Fellow, Single-Cell and Spatial Transcriptomics in Immune-Mediated Inflammatory Diseases We are seeking a highly motivated Postdoctoral Research Fellow to join a cross-functional translational research team focused on understanding the cellular and tissue mechanisms underlying chronic immune-mediated inflammatory diseases. The successful candidate will leverage large-scale single-cell and spatial transcriptomics datasets from human tissue cohorts to identify disease-driving cellular programs, tissue microenvironments, and translational biomarkers. This position offers a unique opportunity to work at the interface of computational biology, immunology, spatial genomics, and translational medicine while contributing to high-impact research programs spanning multiple therapeutic areas. The project aims to transform emerging multi-omics datasets into biological insights, biomarker hypotheses, disease-mechanism models, and translational strategies relevant to immune-mediated inflammatory diseases, including hidradenitis suppurativa (HS) and related disorders. Career Development Opportunities The fellow will: Receive mentorship from experts across computational biology, immunology, translational medicine, and drug discovery. Publish first-author manuscripts in leading scientific journals. Present at major international scientific conferences. Develop expertise in cutting-edge single-cell, spatial, and multi-omics technologies. Build a strong interdisciplinary network across academia and industry. Gain experience translating complex biological datasets into actionable therapeutic insights. Scientific Impact The successful candidate will contribute to the development of comprehensive cellular and spatial atlases of chronic immune-mediated inflammatory diseases, generating insights into disease circuits, tissue microenvironments, biomarker biology, and translational mechanisms. The work is expected to result in multiple high-impact publications and presentations while providing foundational knowledge to support therapeutic discovery and development. This research will help advance our understanding of how cellular programs and tissue organization contribute to disease pathogenesis and therapeutic response, enabling more precise approaches to target discovery, biomarker identification, and patient stratification. Research Objectives The postdoctoral fellow will lead three interconnected research areas: Build Cellular Atlases of Immune-Mediated Inflammatory Diseases Develop comprehensive single-cell transcriptomic references from diseased and comparator tissues. Identify disease-associated immune, epithelial, stromal, and vascular cell states. Harmonize and annotate cellular populations across cohorts and studies. Characterize molecular pathways and regulatory programs that drive disease heterogeneity. Define shared and disease-specific cellular mechanisms across inflammatory conditions. Define Spatial Tissue Niches and Cellular Interactions Analyze spatial transcriptomics datasets to localize disease-associated cell states within tissue architecture. Characterize cellular neighborhoods and multicellular interaction networks in situ. Identify tissue microenvironments associated with inflammation, remodeling, fibrosis, and disease progression. Develop computational frameworks for spatial niche discovery and tissue organization analysis. Integrate spatial and single-cell datasets to generate mechanistic hypotheses regarding disease biology. Mechanism of Action and Biomarker Discovery 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 linked to clinical heterogeneity. Integrate multimodal datasets to construct disease-mechanism and translational biology models. Generate hypotheses supporting therapeutic target evaluation and precision medicine approaches. Key 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 closely with immunologists, translational scientists, clinicians, and computational researchers. Lead publication-quality analyses and prepare manuscripts for peer-reviewed journals. Present findings at internal scientific forums and external conferences. Contribute to the development of innovative analytical methods and reusable computational resources. Collaborate across Data, AI and Genome Sciences (DAGS), Immunology Discovery, and Translational Medicine teams. Qualifications Education Minimum Requirement: Must currently hold a PhD 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. Required Skills and Experience: 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 collaborative skills. Preferred Skills and Experience: Single-cell omics analysis workflows (e.g., Seurat, Scanpy, scVI, CellChat, MultiNicheNet, or related tools). Spatial transcriptomics platforms such as Xenium, CosMx, Visium, MERFISH, or related technologies. Background in immunology, inflammatory diseases, tissue biology, or translational research. Machine learning, network analysis, systems biology, or multimodal data integration. Familiarity with cloud computing and high-performance computing environments. Working with large-scale human cohort datasets. The salary range for this role is: $82,000-$92,000 This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs. The successful candidate will be eligible for annual bonus and long-term incentive, if applicable. </