Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : Aix Marseille University / Centuri
Durée : 12 mois
Contact : badihghattas@gmail.com
Date limite de publication : 2022-05-06

Contexte :
Regulation of gene transcription is accomplished by proximal (promoters) and distal (enhancers) regulatory elements. However, a strict dichotomy model is now challenged and a major question in the field is to define the genetic determinants of the different regulatory activities. The Spicuglia team has previously identified Epromoters as cis regulatory elements with both enhancer and promoter (E/P) activities and is currently using high-throughput approaches to evaluate both activities in thousands of wild-type and mutant DNA sequences. In this project, we will build a sequence-based deep learning model of Epromoters to unravel the genetic determinants of enhancer vs. promoter activities. The model will be challenged and refined in back-and-forth exchanges between model predictions, experimental validation and synthetic generation of Epromoters.

Sujet :
Combined deep learning and synthetic-based approaches to unravel the genetic determinants of enhancer versus promoter activity of Epromoters

Profil du candidat :
The postdoc candidate should have a PhD in bioinformatics or related fields, with a solid background in computer science, statistics and/or mathematics. The candidate should be interested in “omics” data analyses, genomics and gene regulation. Proven previous experience in manipulating NGS data and/or deep learning and in collaborations with experimental biologists is an advantage.

Formation et compétences requises :
Machine learning, Deep learning, bioinformatics.

Adresse d’emploi :
Aix Marseille University, campus de Luminy.

Document attaché : 202203301342_Post-doc CENTURI_offer.pdf