**Job Description**
This Postdoctoral Associate position is within the Computational Microscopy Imaging Lab (CMIL), led by Dr. Pinaki Sarder, and is part of a federally funded R01 initiative. The role focuses on the integration of spatial omics, transcriptomics, and histology data, with a strong emphasis on computational analysis of single-cell RNA sequencing and cutting-edge spatial omics data from technologies like 10X Visium and Xenium. The researcher will lead efforts in integrating multi-modal biomedical data with histological and clinical features using machine learning and foundational modeling, supporting disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A strong publication record and background in computational biology, molecular omics, and AI are essential for this role.
**Skills & Abilities**
• Demonstrated experience in spatial omics data analysis (e.g., 10X Visium, Xenium)
• Proficiency with single-cell RNA-seq tools and workflows (e.g., Seurat, Scanpy)
• Knowledge of machine learning or foundational modeling applied to biomedical data
• Strong publication record in computational biology, systems biology, or AI for healthcare
• Experience in high-dimensional data integration and reproducible workflow development
**Qualifications**
Required Degree(s) in:
• Bioinformatics
• Computational Biology
• Biomedical Engineering
• Data Science
• or a related field
**Experience**
Other:
• A transcript will not be considered “official” if a designation of “Issued to Student” is visible.
• Degrees earned from an education institution outside of the United States are required to be evaluated by a professional credentialing service provider approved by the National Association of Credential Evaluation Services (NACES).
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