Research Internship: Protein Networks & Drug Repurposing

SFBI; Paris; On-site; EUR 10,000 - 13,000; Full time

Benefits offered by this job

6-month paid internship (stipend)

Access to HPC resources

Interdisciplinary collaboration

Subsidized meals

Job summary

SFBI in Paris invites applications for a 6-month paid internship focused on learning representations of protein interaction networks using deep learning. The project explores drug repurposing and safety assessment within a collaborative DSIMB/BIGR setting at Hôpital Necker, Paris.

You will preprocess data, evaluate models, and develop algorithms to map multi-protein interactions, working closely with computational biology and deep learning experts.

Qualifications

• Must be enrolled in a Master’s program in Computer Science, Bioinformatics, or a related field.
• Strong programming skills in Python and solid coding practices.
• Practical experience with deep learning frameworks (e.g., PyTorch or TensorFlow).
• Good knowledge of computational biology.

Responsibilities

• Collect and preprocess publicly available protein interaction datasets and related biological data.
• Evaluate existing deep learning models for predictive performance on PPI networks.
• Develop a framework to learn representations of multi-protein interactions.
• Develop, train, and validate a deep learning model for reconstruction of protein interaction networks.
• Validate model predictions for drug repurposing and off-target interaction scenarios.

Skills

Python

PyTorch

TensorFlow

Bioinformatics

Education

Master's degree in Computer Science, Bioinformatics, or related field

Job description

SFBI in Paris invites applications for a 6-month paid internship focused on learning representations of protein interaction networks using deep learning. The project explores drug repurposing and safety assessment within a collaborative DSIMB/BIGR setting at Hôpital Necker, Paris.

You will preprocess data, evaluate models, and develop algorithms to map multi-protein interactions, working closely with computational biology and deep learning experts.