Postdoc: Single-Cell & Spatial Genomics Bioinformatics

University of Pennsylvania; United States; On-site; USD 52,000 - 70,000; Full time

Job summary

University of Pennsylvania invites applications for a postdoctoral fellow to study inflammatory processes related to diabetes, aging, and tissue health. The role combines leading scRNA-seq and Xenium bioinformatics with a project-led image-analysis study of adhesion molecules in vivo.

The fellow will work across computational and experimental teams, gaining substantial intellectual ownership, and contributing to translational research in a collaborative Penn environment.

Qualifications

• PhD/MD/DMD/DVM or equivalent doctoral degree in a relevant field.
• Experience bioinformatic analysis of scRNA-seq or spatial transcriptomic data, with leadership potential for the single-cell/Xenium 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.

Responsibilities

• Lead bioinformatic studies using scRNA-seq and Xenium datasets.
• Serve as project leader for quantitative image analysis of adhesion molecules in vivo.
• Develop and apply analytical approaches, interpret spatial relationships within tissues.
• Present findings and write first-author papers; collaborate with experimental colleagues.

Skills

Bioinformatics leadership

R programming

Single-cell analysis workflows

Interpretation of biological data

Independent collaboration

Scientific writing

Education

PhD/MD/DMD/DVM or equivalent

Tools

Seurat

R packages

Job description

University of Pennsylvania invites applications for a postdoctoral fellow to study inflammatory processes related to diabetes, aging, and tissue health. The role combines leading scRNA-seq and Xenium bioinformatics with a project-led image-analysis study of adhesion molecules in vivo.

The fellow will work across computational and experimental teams, gaining substantial intellectual ownership, and contributing to translational research in a collaborative Penn environment.