Postdoc, machine learning applied to transcriptomic data in a model of metastasis. H/F

Task

We have currently generated a wealth of transcriptomic data (mRNA-seq, ONT long-read seq) and epigenetic data to dissect the epithelial to mesenchymal transition underlying tumor progression and metastasis. We will integrate this data as well as explore long range interactions that may regulate RNA processing through direct contact with enhancers. We are therefore looking for very motivated, hard-working and creative candidates with strong background in computer science or statistics to explore RNA regulation through epigenetic and long range interactions. Previous expertise in RNA processing and/or epigenetics will be positively evaluated. Activités

Main activities

- Develop novel methodologies to analyze and integrate sequencing data - Apply state of the art machine learning and statistical methods to produce novel insights into gene regulation during EMT - Help write grant requests and research articles

Requirements

We are therefore looking for very motivated, hard-working and creative candidates with a background in computer science or statistics to explore RNA regulation and especially intron retention. Previous expertise in RNA processing and/or epigenetics will be positively evaluated.

Context

Our group uses machine learning approaches to integrate omics data to understand normal biology and disease. We have a strong background in applying novel algorithmic approaches to better understand mechanisms of gene expression and RNA processing (Middleton et al., Genome Biology, 2017; Wong et al., Nature Comms, 2017; Wong et al., Cell, 2013).