Post Doctoral.Post Doctoral.Associate

University of Pittsburgh • Pittsburgh, PA

3d • Onsite

About The Position

We are seeking a highly skilled and motivated postdoctoral researcher to join a computational biology program at the Vascular Medicine Institute (VMI), University of Pittsburgh, School of Medicine, Department of Medicine. The successful candidate will contribute to a research program focused on developing generalizable computational frameworks for clinical imaging analysis, imaging–omics integration, multi-cohort harmonization, and mechanism-guided therapeutic discovery. The project will integrate large-scale clinical imaging, genomics, proteomics, and other high-dimensional clinical datasets across population cohorts. The overall goal is to model disease vulnerability across cardiovascular, pulmonary, neurological, and systemic disease contexts, and connect population-scale computational discoveries to biologically interpretable mechanisms and therapeutic hypotheses. Our research group operates at the intersection of data science, systems biology, and vascular medicine within the highly interdisciplinary environment of the VMI. We are dedicated to bridging the gap between “big data” and clinical translation by combining computational innovation with experimental and clinical collaboration.

Requirements

• Ph.D. in biomedical image analysis, computational biology, biostatistics, data science, physics or a related discipline.
• Experience in statistical learning (demonstrable project in the relevant field)
• Demonstrated programming skills in Python, R, or related scientific computing environments.
• A strong publication record in the relevant field
• Strong motivation to successfully complete complex, collaborative projects.

Responsibilities

• Harmonization and analysis of imaging data across cohorts and imaging modalities, including MRI, CT, echocardiography, and other clinical imaging platforms.
• Integration of multi-modal datasets including imaging, clinical, genetic and proteomic.
• Development of machine/deep learning tools to model organ vulnerability, disease progression, and clinically relevant outcomes.
• Utilize computational tools for drug prediction.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree