Bioinformatics Engineering Jobs in Pittsburgh, PA

Description
A postdoctoral position is available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania. The fellow will study inflammatory processes and how they impact the skin, mucosa, skeleton and periodontium in the context of diabetes, aging or other pathologic conditions. The position has two primary, complementary components: (1) leading bioinformatic studies using single-cell RNA sequencing (scRNA-seq) and 10x Genomics Xenium spatial transcriptomic datasets; and (2) serving as project leader for a quantitative image-analysis study examining the spatial distribution and tissue organization of adhesion molecules in in vivo specimens. The fellow will work closely with investigators who conduct complementary experimental studies and will have substantial intellectual ownership of both areas of research. The goal is to identify mechanisms of disease and potential therapeutic targets.
Research Focus
Our research examines how diabetes changes cell signaling, differentiation, immune-stromal interactions, and tissue repair. Projects span several tissues, disease models, species, and experimental platforms. Component 1 - Single-cell and spatial genomics bioinformatics: The fellow will lead bioinformatic studies using scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets to identify disease-associated cell states, transcriptional programs, and spatially organized cellular responses. Component 2 - Quantitative image analysis: The fellow will serve as project leader for a study examining the spatial distribution, cellular localization, and tissue organization of adhesion molecules in in vivo specimens. This component will involve development and application of quantitative image-analysis approaches and interpretation of spatial relationships within tissues.
Across the bioinformatics component, the fellow will address spatial signaling networks, cell-cell communication, and changes in cell state over time. Projects may include regulatory network inference, pseudotime analysis, and machine-learning approaches when these methods are scientifically appropriate. Where scientifically informative, results from the bioinformatics and image-analysis components may be integrated to relate molecular and cellular states to adhesion-molecule distribution.
Key Responsibilities
• Lead bioinformatic studies using scRNA-seq and 10x Genomics Xenium spatial transcriptomic datasets.
• Serve as project leader for quantitative image analysis of in vivo specimens to characterize the spatial distribution, cellular localization, and tissue organization of adhesion molecules.
• Develop clear, reproducible computational workflows.
• Perform quality control, data integration, cell annotation, and differential expression analysis.
• Conduct pathway, trajectory, state-transition, and ligand-receptor analyses.
• Integrate multiomic, cross-species, and cross-cohort datasets.
• Integrate transcriptomic data with imaging, histologic, and phenotypic measurements.
• Create clear figures and communicate results to computational and experimental collaborators.
• Help define analytical strategy and interpret biological findings.
• Present results and prepare first-author manuscripts.
• Contribute to grant development and collaborative studies.
Computational Environment
For the single-cell and spatial genomics bioinformatics component, a major focus will be analysis of 10x Genomics Xenium spatial transcriptomic and scRNA-seq datasets. The primary environment uses R, Seurat, and related tools. The fellow may use other validated methods when they improve the analysis. The image-analysis component will use appropriate quantitative imaging and spatial-analysis tools selected according to the specimens, imaging modalities, and scientific questions.
Research Environment and Career Development
The Graves laboratory combines computational discovery with in vivo models, human specimens, histology, flow cytometry, immunofluorescence, and in vitro validation. Relevant experimental systems include genetically engineered mouse models, diabetic and aging models, primary mouse and human cell cultures, and molecular perturbation studies. The fellow will have substantial intellectual ownership of both major components of the position, including leadership of scRNA-seq and Xenium bioinformatic studies and project leadership for the image-analysis study. This includes selecting analytical approaches, leading data analysis, presenting findings, and writing first-author papers. Dr. Graves will provide direct scientific mentoring and regular project guidance. The fellow will also work with collaborators and shared-resource specialists across the University of Pennsylvania. Penn core facilities provide support in single-cell and spatial genomics, biostatistics, imaging, histology, and quantitative analysis. The position offers training at the interface of computational biology, genomics, diabetes, inflammation, tissue repair, mouse genetics, and translational research. The goal is to support scientific independence, strong publications, grant development, and preparation for an academic or industry career.
Qualifications
Required Qualifications
• A PhD, MD, DMD, DVM, or equivalent doctoral degree in a relevant field.
• Hands-on experience with bioinformatic analysis of scRNA-seq or spatial transcriptomic data, with experience applicable to leadership of the single-cell/Xenium bioinformatics component.
• Strong skills in R and modern single-cell analysis workflows.
• Ability to interpret results in a biological and disease context.
• Ability to work independently and collaborate across disciplines.
• Clear scientific writing and communication skills.
Relevant fields include bioinformatics, computational biology, genomics, biostatistics, systems biology, molecular or cell biology, immunology, bioengineering, diabetes biology, skeletal biology, computer science, statistics, data science, or a related discipline.
Preferred Qualifications Include Experience in the Following Areas:
• Spatial transcriptomics.
• Seurat and related R packages.
• Multiomic, multi-species, or cross-cohort integration.
• Trajectory or pseudotime analysis, cell-cell communication analysis, or regulatory network inference.
• Quantitative or spatial image analysis of immunofluorescence, histologic, or related in vivo imaging datasets, particularly experience suitable for independently leading an image-analysis project.
• In vitro or in vivo validation experiments.
• Integrating molecular data with imaging data.
Selected Publications
• Diabetes exacerbates destructive inflammation by activating the CD137L-CD137 axis. Journal of Clinical Investigation. PMID: 41379565.
• Single Cell Sequencing Identifies Distinct Cellular Alterations in Impaired Aged and Diabetic Wounds. Aging Cell. PMID: 41189300.
• Ko KI et al. NF-kappaB perturbation reveals unique immunomodulatory functions in Prx1-positive fibroblasts that promote development of atopic dermatitis. Science Translational Medicine. 2022. PMID: 35108061.
Application Instructions
Funding: The principal investigator has grant support through 2031.
Start date: Available immediately following interviews and reference review.
Application materials: Submit a curriculum vitae, a brief statement describing research experience and future interests, and the names and contact information of three references.
Contact: Jen East, jeneast@upenn.edu