Research Associate: Kidney & CV Metabolic Disease

University of Pennsylvania; Philadelphia (Philadelphia County); On-site; USD 70,000 - 110,000; Full time

Job summary

University of Pennsylvania is seeking a Research Associate in the Division of Renal-Electrolyte and Hypertension to study molecular and cellular mechanisms underlying kidney and cardiovascular-metabolic diseases. The role emphasizes diabetic kidney disease and responses to SGLT2 inhibitors, using single-cell and spatial omics along with functional validation.

Candidates will design and execute experiments, analyze complex datasets, and collaborate with multidisciplinary teams to advance

Qualifications

• MD or MD/PhD or equivalent degree required.
• Experience in experimental design and advanced molecular biology techniques.
• Experience with genetically modified mouse models and computational data interpretation.
• Background in kidney, cardiovascular-metabolic disease, genetics or genomics is essential.

Responsibilities

• Design and conduct biomedical research on kidney and cardiovascular-metabolic diseases.
• Utilize genetically modified mouse models and perform molecular biology techniques.
• Perform computational analyses of human genetics, single-cell RNA sequencing, and spatial transcriptomics data.
• Integrate multi-omics data to identify disease mechanisms and therapeutic responses.

Skills

Molecular biology

Genomics

Computational analysis

Single-cell RNA-seq

Genetically modified models

Experimental design

Spatial transcriptomics

Multi-omics

Gene expression analysis

Primary cell culture

Education

MD or MD/PhD

Tools

Genetically modified mouse models

Job description

University of Pennsylvania is seeking a Research Associate in the Division of Renal-Electrolyte and Hypertension to study molecular and cellular mechanisms underlying kidney and cardiovascular-metabolic diseases. The role emphasizes diabetic kidney disease and responses to SGLT2 inhibitors, using single-cell and spatial omics along with functional validation.

Candidates will design and execute experiments, analyze complex datasets, and collaborate with multidisciplinary teams to advance