At CEA-Leti we have validated a video-lens-free microscopy platform by performing thousands of hours of real-time imaging observing varied cell types and culture conditions (e.g.: primary cells, human stem cells, fibroblasts, endothelial cells, epithelial cells, 2D/3D cell culture, etc.). And we have developed different algorithms to study major cell functions, i.e. cell adhesion and spreading, cell division, cell division orientation, and cell death. The research project is to extend the analysis of the datasets produced by lens-free video microscopy. The objective is to study a real-time cell tracking algorithm to follow every single cell and to plot different cell fate events as a function of time. To this aim, researches will be carried on segmentation and tracking algorithms that should outperform today’s state-of-the-art methodology in the field. In particular, the algorithms should yield good performances in terms of biological measures and practical usability. This will allow us to outperform today’s state-of-the-art methodology which are optimized for the intrinsic performances of the cell tracking and cell segmentation algorithms but fails at extracting important biological features (cell cycle duration, cell lineages, etc.). To this aim the recruited person should be able to develop a method that either take prior information into account using learning strategies (single vector machine, deep learning, etc.) or analyze cells in a global spatiotemporal video. We are looking people who have completed a PhD in image processing, with skills in the field of microscopy applied to biology.