Spatial Transcriptomics Scientist for Kidney Disease

University of Washington Medical Center; Seattle (WA); On-site; USD 80,000 - 133,000; Full time

Job summary

University of Washington Medical Center's Division of Metabolism, Endocrinology and Nutrition invites applications for a Research Scientist/Engineer 3 to support spatial transcriptomics studies.

The role focuses on processing and analyzing spatial transcriptomics data from kidney tissue using 10x Genomics platforms, with emphasis on cell type deconvolution, integration with scRNA-seq, and pathway analyses; mentoring and manuscript contributions are expected.

Qualifications

• Bachelor’s degree in bioinformatics, computational biology, biostatistics, data science or related field with four years of relevant experience.
• Advanced knowledge of basic statistical principles in medical research.
• Experience with spatial transcriptomics platforms (10x Genomics Visium, Visium HD, Xenium).
• Ability to analyze complex problems and communicate results clearly.

Responsibilities

• Process and quality-control spatial transcriptomics data from human and mouse kidney tissue (Visium/Visium HD/Xenium Prime 5K).
• Perform cell type deconvolution and integrate spatial data with single-cell RNA-seq references.
• Conduct differential expression and pathway enrichment analyses; identify spatially variable genes.
• Collaborate on publications and mentor team members; prepare abstracts and presentations.

Skills

Spatial transcriptomics

Data analysis

R

Python

Team mentorship

Communication

Education

Bachelor’s degree in Bioinformatics/Computational Biology/Biostatistics/Data Science or related field

Tools

Space Ranger

Seurat

RCTD

spacexr

GSEA

ImageJ

Job description

University of Washington Medical Center's Division of Metabolism, Endocrinology and Nutrition invites applications for a Research Scientist/Engineer 3 to support spatial transcriptomics studies.

The role focuses on processing and analyzing spatial transcriptomics data from kidney tissue using 10x Genomics platforms, with emphasis on cell type deconvolution, integration with scRNA-seq, and pathway analyses; mentoring and manuscript contributions are expected.