Assistant Scientist - Nephrology - Quantitative Health

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

Assistant Scientist - Nephrology - Quantitative Health

• University of Florida
• United States

About University of Florida

At the University of Florida, we are a people of purpose. We're committed to challenging convention and ourselves. We see things not as they are, but as they could be. And we strive for a greater impact: one measured in people helped...

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

Job Description

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.

Salary is negotiable and commensurate with education and experience.

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

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

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