Description
Date depot: 7 avril 2023
Titre: Machine learning for super resolution microscopy
Directrice de thèse:
Nataliya SOKOLOVSKA (LCQB)
Encadrante :
Judith MINÉ-HATTAB (IBPS)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Sciences de l’information et sciences du vivant
Resumé: Development of imaging techniques, especially, microscopic imaging techniques, has made it possible to visualize various biological phenomena. Biologists often investigate images manually, it requires a lot of effort, time, and concentration. Note that it is hardly possible to analyse a big amount of data reliably manually. At the same time, research on automated image processing is a very active domain of artificial intelligence and machine learning, and a number of efficient image processing and pattern recognition approaches to analyse biological images appeared recently. Our ultimate goal is to propose a novel and specific method to stratify single molecule microscopy images, so that the proposed approach ensures rapidity and reproducibility of bioimage analysis. To provide expertise and supervision in fundamental biology and machine learning, the PhD will be co-supervised by Nataliya Sokolovska and Judith Miné-Hattab, LCQB, Sorbonne University.
Doctorant.e: Park Junwoo