Assistant Scientist - Nephrology - Quantitative Health

Job no: 540195

Work type: Non-Tenure-Track Faculty

Location: Main Campus (Gainesville, FL)

Categories: Medicine/Physicians

Department: 29051308 - 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. Additionally, candidates must have:

• Demonstrated expertise in deep learning and AI frameworks (e.g., PyTorch, TensorFlow) and large-scale image analysis libraries (e.g., large_image 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/GitHub)
• 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.

The successful candidate will contribute to 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 position offers an exceptional 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.

Research Focus Areas

• 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

About The University Of Florida

The University of Florida, a member of the Association of American Universities (AAU), is the largest and most comprehensive public university in the State of Florida, with large undergraduate, graduate, and postgraduate educational programs. The UF Health Science Center and its six colleges are co-located on the Gainesville campus, with additional teaching, research, and patient care sites in Jacksonville, Orlando, and other sites across Florida and internationally. Resources available for professional development include leadership, education, and research tracks within a Clinical and Translational Science Institute (CTSI), formal mentorship programs, and supported opportunities for teaching and research.

Expected Salary

Salary is negotiable and commensurate with education and experience.

Minimum Requirements

Preferred Qualification:

• 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

Special Instructions To Applicants

Interested candidates are encouraged to submit a current curriculum vitae (CV) via the University of Florida's online application system. The ideal candidate will be highly motivated, scientifically rigorous, and enthusiastic about working in an interdisciplinary and collaborative team environment.

Application must be submitted by 11:55 p.m. (ET) of the posting end date.

Health Assessment Required:No

Advertised: 11 Jun 2026 Eastern Daylight Time

Applications close: 25 Jun 2026 Eastern Daylight Time

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