Job:PhD position: Combined deep learning and synthetic-based approaches to unravel the genetic determinants of enhancer versus promoter activity of Epromoters (Marseille, France)
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20 days ago

We are proposing a PhD position to build a sequence-based deep learning model of Epromoters to unravel the genetic determinants of enhancer vs. promoter activities.

Details of the project can be found here: https://centuri-livingsystems.org/phd2023-02/

This PhD project is funded by the Turing Centre for Living Systems (CENTURI). The deadline of application is: February 16, 2023 and the application procedure is explained here: https://centuri-livingsystems.org/recruitment/

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.

The PhD candidate should have a Master in bioinformatics or related fields at the beginning of the project in September 2023, with an interdisciplinary background in biology, computer science, statistics and/or mathematics. The candidate should be interested in “omics” data analyses, genomics and gene regulation. Knowledge in manipulating NGS data and/or deep learning is an advantage.

Thank you for sharing this offer with motivated candidates.

synthetic-biology variants genetics Cis-regulatory-elements machine-learning • 347 views
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