Postdoctoral Position, Geometric Deep Learning at Télécom Paris

ELLIS; Paris; Sur place; EUR 38 000 - 48 000; Plein temps

Résumé du poste

A leading research institute is offering a postdoctoral position in geometric deep learning and graph machine learning. This role involves working on innovative research projects focusing on theoretical guarantees and developing models for structured data. Candidates should have a Ph.D. (or near completion) in related fields and a strong publication record. The position is for 1 year, renewable, and offers collaboration opportunities with experts. Applications are reviewed on a rolling basis.

Qualifications

• PhD (or near completion) in applied mathematics, computer science, or electrical engineering.
• Strong background in machine learning with interest in theoretical aspects.
• Experience in geometric deep learning and/or graph signal processing.
• A strong publication record in machine learning, graph signal processing, or related fields is expected.
• Good programming skills in Python / PyTorch

Responsabilités

• Work on geometric deep learning and graph machine learning under supervision.
• Engage in research collaboration with leading experts.
• Collaborate with international partners and publish results in top venues.
• Contribute to the broader research program and mentor junior researchers where appropriate.

Connaissances

Machine learning

Geometric deep learning

Graph signal processing

Python

PyTorch

Publication record

Formation

PhD in applied mathematics, computer science, or related fields

Outils

Python

PyTorch

Description du poste

We are opening a postdoctoral position at Télécom Paris (Institut Polytechnique de Paris) within the LTCI laboratory, in the area of geometric deep learning and graph machine learning.

This postdoctoral position is part of a broader research program focused on understanding why and when geometric inductive biases improve learning, and on designing continuous and higher-order models with theoretical guarantees. The project builds on recent work on continuous product graph neural networks and continuous simplicial neural networks, with the goal of advancing principled and scalable learning methods for structured data.

The research lies at the intersection of:

• Geometric and topological deep learning
• Graph signal processing
• Continuous and PDE-inspired learning models
• Stability, expressivity, and generalization analysis of geometric deep learning models

The postdoc will work under the primary supervision of Jhony H. Giraldo, with opportunities for scientific interaction and collaboration with:

• Antonio Ortega (University of Southern California)
• Dorina Thanou (EPFL)

Candidate profile (indicative):

• PhD (or near completion) in applied mathematics, computer science, electrical and computer engineering, or related fields.
• Strong background in machine learning, with interest in theoretical aspects.
• Experience in geometric deep learning and/or graph signal processing, including graph neural networks and/or higher-order neural network models.
• A strong publication record in machine learning, graph signal processing, or related fields is expected.
• Good programming skills in Python / PyTorch
• Ability to work independently and propose original research directions

Practical information:

• Duration: 1 year, renewable.
• Funding: ANR, the French National Research Agency JCJC project DeSNAP.

Applications are reviewed on a rolling basis.