Postdoctoral Fellow in Single-Cell and Spatial Genomics Bioinformatics

Postdoctoral position available in the laboratory of Dr. Dana T. Graves at the University of Pennsylvania, Penn Dental Medicine.

About the role

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 focuses on leading computational analysis of single-cell, spatial, and multiomic datasets, working closely with investigators conducting complementary experimental studies. This is an opportunity to take substantial intellectual ownership of a disease-focused computational research program with the goal of identifying 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. The fellow will identify disease-associated cell states and transcriptional programs, addressing 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.

Responsibilities

• Lead analysis of single-cell RNA-seq and spatial transcriptomic data.
• 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.

A major focus will be analysis of 10x Genomics Xenium spatial transcriptomic and single-cell RNA-seq datasets. The primary environment uses R, Seurat, and related tools. The fellow may use other validated methods when they improve the analysis.

Qualifications

• A PhD, MD, DMD, DVM, or equivalent doctoral degree in a relevant field.
• Hands-on experience with bioinformatic analysis of single-cell RNA-seq or spatial transcriptomic data.
• 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

• 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.
• Image analysis.
• In vitro or in vivo validation experiments.
• Integrating molecular data with imaging data.