Post Doctoral.Post Doctoral.Associate
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Before you apply
LocationPittsburgh, PA
SeniorityEntry
Company size10,000+ people
First seenOct 18, 2026 · 2w ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
PhD in statistical learning, computational biology, systems biology, data science, or a related quantitative discipline
Fluency in Python programming language
Experience with high-throughput omics data analysis
Strong foundation in statistics, machine learning, mathematics, and biological data interpretation
Skills
Python
AI
deep learning
RNA-seq
single-cell RNA-seq
epigenomics
proteomics
metabolomics
genomics
Postdoctoral Associate in AI-Driven Omics Analysis and Drug Discovery at the Vascular Medicine Institute, Department of Medicine, School of Medicine, University of Pittsburgh
The Vascular Medicine Institute at the University of Pittsburgh is seeking a highly motivated postdoctoral associate to join a computational research program focused on AI-driven omics analysis, systems biology, and therapeutic discovery. The successful candidate will develop and apply computational and deep learning approaches to understand how complex biological stressors drive molecular, cellular, and organ dysfunction, and to identify therapeutic strategies for disease treatments. The project will involve large-scale analysis and integration of transcriptomics, single-cell RNA-seq, and other omics modalities.
Our research group operates at the intersection of computational biology, systems medicine, and translational science within the Vascular Medicine Institute. We focus on integrating large-scale omics data with mechanistic modeling to uncover systemic drivers of disease. This position provides a unique opportunity to develop broadly applicable computational frameworks for understanding organ dysfunction and therapeutic intervention.
Responsibilities
Major duties:
•
Develop and apply AI/deep learning models for omics-based disease mechanism discovery and therapeutic prediction.
•
Analyze and integrate high-throughput omics datasets, including bulk RNA-seq, single-cell/nuclei RNA-seq, epigenomics, proteomics, metabolomics, and genomics.
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Develop and refine drug-repurposing and target-prioritization algorithms using disease signatures, perturbation datasets, signaling networks, and drug-target databases.
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Build computational pipelines for preprocessing, quality control, harmonization, and integration of public and internal omics datasets.
Requirements
Minimum Requirements for the candidate:
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PhD in statistical learning, computational biology, systems biology, data science, or a related quantitative discipline.
•
Fluency in Python programming language
•
Experience with high-throughput omics data analysis
•
Strong foundation in statistics, machine learning, mathematics, and biological data interpretation
Hiring process
Interested applicants should apply via join.pitt.edu Requisition #26003687
Legal
The University of Pittsburgh is an equal opportunity employer / disability / veteran.