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A postdoctoral position is available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania School of Dental Medicine, Department of Periodontics.
The fellow will investigate how cellular senescence and inflammatory signaling intersect to influence aging-related periodontal disease using mouse models and validation in human tissue. The role integrates single-cell RNA sequencing (scRNA-seq), 10x Genomics Xenium spatial transcriptomics, quantitative imaging, and molecular
A postdoctoral position is available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania School of Dental Medicine, Department of Periodontics. The fellow will investigate mechanisms by which cellular senescence and inflammation intersect to alter age-associated pathogenesis. The position integrates experimental biology with single-cell RNA sequencing (scRNA-seq), 10x Genomics Xenium spatial transcriptomics, quantitative imaging, and molecular and cellular validation to define disease-associated cell states, inflammatory signaling networks, and potential therapeutic targets.
The research program will examine how senescent cell states and inflammatory signals influence pathology that enhances aging-linked pathology using a periodontal mouse model and human tissue validation. The fellow will participate in in vivo studies and analyses of scRNA-seq and 10x Genomics Xenium datasets to identify senescence-associated and inflammation-associated cell states, transcriptional programs, cell-cell communication networks, spatially organized cellular responses and mechanisms that lead to greater bone resorption. The analyses may include pathway analysis, trajectory and pseudotime analysis, regulatory-network inference, and ligand-receptor analysis. Experimental studies of aging and senescence will include genetically engineered mouse models, primary mouse and human cell cultures, molecular perturbation studies, histology, immunofluorescence, flow cytometry, quantitative image analysis, and other approaches used to validate computational findings and define mechanisms linking aging, senescence and inflammation with altered tissue function.
For single-cell and spatial genomics studies, the primary computational environment uses R, Seurat, and related tools, with other validated methods used when they improve analytical rigor or biological interpretation. Quantitative imaging and spatial-analysis tools will be selected based on the specimens, imaging modalities, and scientific questions. Candidates with strong experimental backgrounds will have opportunities to develop additional expertise in computational analysis, while candidates with strong computational backgrounds will have opportunities to gain experience with biological validation and experimental systems.
The Graves laboratory integrates computational discovery with in vivo models, human specimens, histology, flow cytometry, immunofluorescence, primary cell culture, molecular perturbation studies, and quantitative analysis. The fellow will have substantial intellectual ownership of their project, including development of experimental directions, selection of analytical approaches, interpretation and presentation of findings, preparation of first-author manuscripts, and participation in grant writing. Development of independent fellowship applications is encouraged.
Dr. Graves will provide direct scientific mentoring and regular project guidance. The fellow will collaborate with investigators and shared-resource specialists across the University of Pennsylvania. Penn core facilities support single-cell and spatial genomics, biostatistics, imaging, histology, and quantitative analysis. The position is designed to support scientific independence, strong publications, grant development, and preparation for an academic or industry career.
The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.