Deep learning and synthetic data generation methods for automated analysis of volumetric images of human and mouse brains acquired by light-sheet fluorescence microscopy

The research activity will focus on the processing and analysis of large volumetric images acquired bylight-sheet fluorescence microscopy from human and mouse brains, within the Human Optical BrainMapping project. The work will involve the development and implementation of advanced computationalmethods for automated image analysis, with particular attention to segmentation, quantitativeinformation extraction, and comparison across datasets acquired under different experimentalconditions.

Selection process

titles evaluation Master degree in Biomedical EngeneeringExpertise in analysis of volumetric fluorescence images, programming, deep learning, and machine learning.Experience with Python and major libraries for image processing and neural networks; knowledge ofsegmentation methods, domain adaptation and/or generative models for synthetic data. Experience with lightsheetfluorescence microscopy images, large 3D datasets, annotated ground truth management, and highperformancecomputing environments will be considered an advantage

Job posting website Original MUR listing

Back to jobs