Assistant Scientist - Nephrology - Quantitative Health
Behavioral Health Market Context
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Benefits
Salary is negotiable and commensurate with education and experience
Job Description
Assistant Scientist - Nephrology - Quantitative Health
Job No: 540195
Work Type: Full Time
Location: Main Campus (Gainesville, FL)
Categories: Medicine/Physicians
Department: 29051308 - MD-MED QUANTITATIVE HEALTH
Job Description
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
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.
Expected Salary:
Salary is negotiable and commensurate with education and experience.
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
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
Applications Close: 25 June 2026
To apply, visit https://explore.jobs.ufl.edu/en-us/job/540195
Our Commitment:
The University of Florida is an Equal Employment Opportunity Employer.
Hiring is contingent on eligibility to work in the U.S. The University of Florida is a public institution and is subject to all requirements under Florida Sunshine and Public Record laws. If an accommodation due to a disability is needed to apply for this position, please call 352-392- 2477 or the Florida Relay System at 800-955-8771 (TDD) or visit Accessibility at UF.
Copyright ©2025 Jobelephant.com Inc. All rights reserved.
Posted by the FREE value-added recruitment advertising agency
jeid-8dd99ac572cf0a4ba17192f9bb6dc1fc
Job No: 540195
Work Type: Full Time
Location: Main Campus (Gainesville, FL)
Categories: Medicine/Physicians
Department: 29051308 - MD-MED QUANTITATIVE HEALTH
Job Description
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
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.
Expected Salary:
Salary is negotiable and commensurate with education and experience.
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
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
Applications Close: 25 June 2026
To apply, visit https://explore.jobs.ufl.edu/en-us/job/540195
Our Commitment:
The University of Florida is an Equal Employment Opportunity Employer.
Hiring is contingent on eligibility to work in the U.S. The University of Florida is a public institution and is subject to all requirements under Florida Sunshine and Public Record laws. If an accommodation due to a disability is needed to apply for this position, please call 352-392- 2477 or the Florida Relay System at 800-955-8771 (TDD) or visit Accessibility at UF.
Copyright ©2025 Jobelephant.com Inc. All rights reserved.
Posted by the FREE value-added recruitment advertising agency
jeid-8dd99ac572cf0a4ba17192f9bb6dc1fc
Qualifications
- •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, 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
- •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, 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
- •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
Responsibilities
- •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
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