There is an open postdoctoral position in the Chemoinformatics and Proteochemometrics group of Dr. Olivier Sperandio and the Structural Bioinformatics unit of Pr. Michael Nilges at the Institut Pasteur in Paris. The position is available for up to three years, with a one-year minimum duration, and is supported by patronage funding from the Dassault Foundation. Our group/unit is a part of the structural biology and chemistry department of Institut Pasteur.
Our research group is dedicated to developing various data-driven computer-aided drug design approaches, utilizing ML/DL methods. We are currently working on multiple drug design projects related to emerging infectious diseases, antimicrobial resistance, and cancer.
The main objective of this project is to enhance the capabilities of our existing tool, InDeep1. The tool is designed to predict functional binding sites on protein surfaces, specifically for drug discovery purposes, using 3D fully convolutional neural networks.
Profile : For this project, we are looking for a highly motivated postdoctoral researcher to develop state-of-the-art deep learning methodologies.
- Strong experience in various deep learning architectures is essential,
- Strong programming skills in python DL libraries are essential (PyTorch, Tensorflow)
- A former experience as a data scientist is stronger recommended,
- A background in computer-aided drug design is a plus,
- High motivation, excellent communication skills, and the ability to work collaboratively are essential.
Position: The position is available immediately and will remain open until filled. The expected start date is June 2023. The position is initially for 1 year, with the possibility of extension up to 3 years. Salary is commensurate with experience according to the institutional guidelines.
To apply: Please send a single pdf file containing a CV, a cover letter summarizing research interests and career goals, and contact information of 2 (or more) references to: email@example.com
- Mallet, V. et al. InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein–protein interactions . Bioinformatics 38, 1261–1268 (2022).