Renal Pathology Postdoc: Multi-Omics & Spatial Proteomics

University of Oxford; Oxford; On-site; GBP 39,000 - 48,000; Full time

Job summary

University of Oxford is seeking a Postdoctoral Researcher to join the Clinical Proteomics Group and Kidney Pathology Group in Oxford to investigate focal segmental glomerulosclerosis (FSGS). You will work with multi-omic and clinical data to identify molecular signatures and immune mechanisms, and contribute to publications and collaborative projects.

The role combines experimental and computational work, using LC-MS and spatial proteomics to advance understanding of kidney disease, with a

Qualifications

• PhD/DPhil in proteomics, biomedical science or bioinformatics required.
• Hands-on experience in renal pathology research, including mouse and cell line models, and tissue processing/histology.
• Experience in spatial/single-cell LC-MS proteomics and transcriptomics is essential.
• Experience operating LC-MS and supporting equipment (timsTOF, Leica LMD7, cellenONE).

Responsibilities

• Integrate multi-omic and clinical datasets to identify molecular profiles linked to patient phenotypes and outcomes.
• Contribute to spatial proteomic analyses of human kidney samples and validate findings in vitro and in vivo.
• Develop and adapt experimental approaches; analyse complex data and contribute to publications and collaborations.

Skills

Omics data generation

Computational analysis

Renal pathology experience

Mouse models

Cell line models

Education

PhD/ DPhil in proteomics or biomedical or bioinformatics

Tools

LC-MS

timsTOF

Leica LMD7

cellenONE

Job description

University of Oxford is seeking a Postdoctoral Researcher to join the Clinical Proteomics Group and Kidney Pathology Group in Oxford to investigate focal segmental glomerulosclerosis (FSGS). You will work with multi-omic and clinical data to identify molecular signatures and immune mechanisms, and contribute to publications and collaborative projects.

The role combines experimental and computational work, using LC-MS and spatial proteomics to advance understanding of kidney disease, with a