The role

We are seeking a highly motivated candidate strongly interested in interdisciplinary science, to join our group and work on a project at the crossroad between Computer Science and Developmental Biology.

The project aims at developing computational methods and models to better understand how robustness to biological noise is achieved during the development of Drosophilaembryos. This will be done by first quantifying and characterising Drosophila embryogenesis variability at the single cell scale.

The successful candidate will be tasked to build statistical representations of the morphogenesis of Drosophila embryos at the single cell scale by combining image analysis, big-data science and data visualisation. To this end, the candidate will first develop novel image and big-data analysis algorithms. These algorithms will be first applied to 3D movies of Drosophila embryos acquired with state-of-the-art light-sheet fluorescence microscopes. The successful candidate will then develop novel algorithms to combine the set of recorded embryos together to build a single-cell scale, in-toto, atlas of Drosophila embryogenesis.

Depending on the advancement of the project and the candidate preferences, the second part of the project will then focus around either integrating complementary single-cell omic data to the atlas or developing machine-learning based methods to analyse, detect and classify cell patterns in the developing embryo.