Assistant Scientist - Nephrology - Quantitative Health

Job No: 540195
Work Type: Full Time
Location: Main Campus (Gainesville, FL)
Categories: Medicine/Physicians
Department:  - MD-MED QUANTITATIVE HEALTH
Classification

Title:

AST SCTST

Classification

Minimum Requirements:

• Applicants must hold a PhD in computer science, electrical engineering, biomedical engineering, or a closely related field, and have completed at least 2‑3 years of postdoctoral research experience.
• Candidates must also demonstrate a strong record of research productivity and scholarly achievement, as evidenced by peer‑reviewed publications.
• Demonstrated expertise in deep learning and AI frameworks (e.g., PyTorch, Tensor Flow) and large‑scale image analysis libraries (e.g.,  by kitware)
• Hands‑on experience with whole slide image analysis and computational pathology workflows
• Proficiency in Python and scientific computing libraries
• Experience with version control systems (e.g., Git/Git Hub)
• Experience with high‑performance computing (HPC) environments and large‑scale data processing

The Department of Medicine at the University of Florida invites applications for a full‑time, non‑tenure‑track faculty position at the Assistant Scientist level. The successful candidate will join the Computational Microscopy Imaging Laboratory (CMIL), directed by Dr. Pinaki Sarder, a research group at the forefront of computational pathology, artificial intelligence, and microscopy image analysis applied to biomedical discovery.

Responsibilities include conducting cutting‑edge research at the intersection of deep learning, digital pathology, and multi‑omics data integration, with a strong emphasis on kidney disease, particularly diabetic kidney disease. The role offers an opportunity to work in a highly collaborative, team‑based research environment and to play a meaningful role in advancing AI‑driven solutions for understanding complex renal pathologies.

• Computational pathology and digital pathology, with application to kidney disease and diabetic kidney disease
• Deep learning and AI model development for histological and microscopy image analysis
• Whole slide image (WSI) analysis and quantitative microscopy
• Spatial transcriptomics and multi‑omics data integration
• High‑performance computing and scalable biomedical data analysis pipelines

• Prior research experience in kidney disease, with specific expertise in diabetic kidney disease highly preferred
• Experience with spatial transcriptomics and multi‑omics data integration
• Familiarity with microscopy imaging modalities relevant to renal pathology
• Track record of or demonstrated potential for independent grant writing and funding acquisition
• Experience mentoring junior researchers, graduate students, or undergraduate trainees
• Strong written and verbal communication skills, with a collaborative research approach

Applications Close: 25 June 2026

We are an Equal Employment Opportunity Employer.