Kidney AI Scientist: Digital Pathology & WSI

University of Florida; Gainesville (FL); On-site; USD 90,000 - 130,000; Full time

Job summary

The University of Florida invites applications for an Assistant Scientist in Nephrology - Quantitative Health in Gainesville. The role sits within the Computational Microscopy Imaging Laboratory (CMIL) and focuses on integrating deep learning, digital pathology, and multi-omics for renal disease research.

The successful candidate will develop AI models for whole slide image analysis, collaborate with clinicians, and contribute to advancing AI-driven solutions for understanding diabetic kidney

Qualifications

• PhD in computer science, electrical engineering, biomedical engineering, or a closely related field.
• 2-3 years of postdoctoral research experience.
• Strong record of research productivity with peer‑reviewed publications.
• Expertise in deep learning and AI frameworks and large-scale image analysis libraries.
• Hands-on experience with whole slide image analysis and computational pathology workflows.
• Proficiency in Python and scientific computing libraries.
• Experience with version control systems (Git/GitHub).
• Experience with high-performance computing environments.

Responsibilities

• Join CMIL to advance deep learning and digital pathology for renal disease research.
• Develop AI models for histology and WSI analysis.
• Integrate multi-omics data in renal disease studies.
• Collaborate in a team-based research environment.

Skills

Deep learning

Python

AI frameworks

Image analysis

HPC

Scientific computing

Communication

Education

PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or related field

Tools

PyTorch

TensorFlow

large_image (Kitware)

Git/GitHub

Job description

The University of Florida invites applications for an Assistant Scientist in Nephrology - Quantitative Health in Gainesville. The role sits within the Computational Microscopy Imaging Laboratory (CMIL) and focuses on integrating deep learning, digital pathology, and multi-omics for renal disease research.

The successful candidate will develop AI models for whole slide image analysis, collaborate with clinicians, and contribute to advancing AI-driven solutions for understanding diabetic kidney