Mots-Clés

machine learning deep learning protein-carbohydrate interactions structural bioinformatics protein structure carbohydrates

Description

Position: Post-doc (18 month)

Starting date: 1st April 2022 (can be discussed)

Name of the project: “Deciphering protein carbohydrate interactions using machine learning approaches”

Project description: The postdoc will participate in work on the project SugarPred, which has recently received funding from the French National Research Agency (ANR). Protein-carbohydrate (PC) interactions play a key role in various biological processes and represent an important therapeutic target. However, as compared to protein-protein interactions or protein interactions with small molecules (drugs), PC interactions are much less investigated due to the difficulties related to their experimental description and high diversity. The main goal of SugarPred is the development of carbohydrate binding site prediction tools through implementation of the most recent machine learning approaches. The candidate will be responsible for the implementation and tuning of a deep learning model for carbohydrate binding site prediction using a new database developed by the team. 

The team: Postdoc will work under supervision of Dr. Tatiana Galochkina (project leader, Assistant Professor at Université de Paris, personal website). He/she will also be helped by a Master student hired in the framework of the project and responsible for accurate data preparation. A second Master 2 student will be hired and co-supervised by Dr. Galochkina and the Postdoc next year. 

What we offer: The DSIMB team has extensive experience in analysis and prediction of diverse protein structural properties and provides an excellent environment for networking as well as access to local and national computational resources. The project is financed by ANR and expected publications will aim at the high-rated journals.

The candidate: The candidate must have a PhD degree in bioinformatics, applied mathematics, computational science or related field. An ideal candidate would have an important background in machine learning model development and experience in dealing with structural bioinformatics problems. Experience in studies of protein-protein or protein-drug interactions would be particularly appreciated.

Contact: The candidates are invited to send their CV accompanied by a short motivation letter to Dr. Galochkina: tatiana.galochkina@u-paris.fr