Kidney AI Scientist: Computational Pathology & Imaging

University of Florida; Gainesville (FL); Presencial; USD 70 000 - 110 000; Tempo integral

Resumo da oferta

University of Florida in Gainesville invites applications for an Assistant Scientist in Nephrology - Quantitative Health. The role emphasizes deep learning, digital pathology, and multi-omics data integration, applying AI-driven image analysis to kidney disease research within CMIL led by Dr.

Pinaki Sarder. Candidates will work in a highly collaborative environment, developing scalable computational workflows for whole slide image analysis and contributing to advancing understanding of diabetic

Qualificações

• PhD in computer science, electrical engineering, biomedical engineering, or related field.

Responsabilidades

• Conduct cutting-edge research in deep learning, digital pathology, and multi-omics data integration.
• Collaborate with the Computational Microscopy Imaging Laboratory (CMIL) on kidney disease-focused projects.
• Develop scalable image analysis pipelines and contribute to AI-driven solutions for renal pathology.
• Publish results in peer-reviewed journals and present at conferences.
• Mentor junior researchers and contribute to grant writing and proposal development.

Conhecimentos

Deep learning

PyTorch

TensorFlow

Image analysis

Python

Git / GitHub

HPC environments

Large-scale data processing

Formação académica

PhD in CS/EE/BME or related field

2–3 years postdoctoral experience

Ferramentas

large_image (Kitware)

Git

GitHub

Descrição da oferta de emprego

University of Florida in Gainesville invites applications for an Assistant Scientist in Nephrology - Quantitative Health. The role emphasizes deep learning, digital pathology, and multi-omics data integration, applying AI-driven image analysis to kidney disease research within CMIL led by Dr.

Pinaki Sarder. Candidates will work in a highly collaborative environment, developing scalable computational workflows for whole slide image analysis and contributing to advancing understanding of diabetic