Topic: Image analysis

The Institut Pasteur de São Paulo seeks a postdoctoral fellow to establish and apply Cell Painting, a high-dimensional image-based morphological profiling assay, to identify how Brazilian natural compounds act against intracellular bacteria. The researcher will combine high-content fluorescence microscopy with image, data and bioinformatics analysis in a host-directed therapeutics project supported by FAPESP.

The EMBL Imaging Centre is recruiting an application specialist to develop, optimise and deliver super-resolution cryogenic light microscopy and cryo-CLEM workflows, support user projects from experimental design through analysis, and train the wider imaging community.

Abstract to follow.

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classi

Deep learning techniques have revolutionized the field of image analysis and played a disruptive role in the ability to quickly and efficiently train image analysis models that perform as well as human beings. This talk will cover the beginnings of the application of deep learning in the field of ophthalmology and vision science, and cover a variety of applications of using deep learning as a method for scientific discovery and latent associations.

Deep learning is an emerging computational approach inspired by the human brain’s neural connectivity that has transformed machine-based image analysis. By using histopathology as a model of an expert-level pattern recognition exercise, we explore the ability for humans to teach machines to learn and mimic image-recognition and decision making. Moreover, these models also allow exploration into the ability for computers to independently learn salient histological patterns and complex ontological relationships that parallel biological and expert knowledge without the need for explicit direction