Mots-Clés

Optimal Transport Autoencoders Single Cell Transcriptomics

Description

We are looking for a postdoctoral fellow in Machine Learning applied to Biology. 

Recent years have witnessed an explosion of data in biology and medicine. Many acquisition techniques, like microscopy or sequencing techniques, provide complementary views of the same system, e.g. an organ, an embryo, a tumor. To understand the dynamics happening at single cell resolution and develop new personalized treatments, we need to integrate these complementary sources of information. To tackle this problem, this project aims at developing new Temporal Data Integration theoretical and computational methods for various complementary acquisition techniques (microscopy, and multi-omics). 

The successful candidate will work jointly within Paul Villoutreix’s interdisciplinary group (http://bioml.lis-lab.fr/) and under the supervision of Thierry Artières within the Machine Learning team of the Computer Science lab in Marseille (https://qarma.lis-lab.fr). The position is part of the Turing Center for Living Systems (https://centuri-livingsystems.org), which is a vibrant interdisciplinary community composed of mathematicians, computer scientists, physicists, …, interested in questions of biology in the scenic Mediterranean city of Marseille, France. 

We are looking for a candidate with a PhD in machine learning, computer science, applied mathematics with strong interest in machine learning and its applications to biology. The candidate will be trained in biology and will benefit from a very strong environment of experts in machine learning. We will also provide support for applications to academic jobs and further career development. 

 More details in the document linked.