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8 novembre 2023

PhD Position: Toward Frugal Machine Learning with Physics-Aware Models

Catégorie : Doctorant

This PhD thesis aims to develop an action recognition algorithm with a minimal ecological footprint. It leverages physics-based machine learning models to simulate complex systems, focuses on geometric and physical invariance, and implements incremental learning for long-term sustainability.


Advisors: Olivier Alata (Professor) and Jordan Frecon-Deloire (Associate Professor)
Host laboratory:
Hubert Curien Lab UMR CNRS 5516, Saint-Étienne, France
Starting date: Early 2024 - at your earliest convenience

Keywords: Physics-based machine learning; Frugal AI; Incremental learning; Action recognition

Context: The present thesis proposal is part of the GreenAI research project resulting from the collaboration between academic (Hubert Curien Lab) and industrial partners (Asygn, DRACULA Technologies). Its main objective is to create autonomous smart sensors benefiting from a low ecological footprint for road monitoring purposes. As such, this project is inherently a vector of cross-cutting developments in physics, machine learning and embedded systems design.

Description: This subject focuses on the development of a learning algorithm for action recognition [1] with a specific emphasis on its environmental impact. On one hand, we aim to minimize its memory usage by learning and imposing both physics and geometric constraints. On the other hand, we will employ incremental learning techniques to ensure its long-term sustainability.

Candidate Profile

Application Candidates must send the following documents to both jordan.frecon.deloire@univ-st-etienne.fr and olivier.alata@univ-st-etienne.fr as soon as possible:

Funding The selected candidate will obtain a 36-month funding. The net salary will be around 1700€. Additional paid teaching activities can be envisaged on demand.

Host Laboratory Created in 2006, the Hubert Curien laboratory is a joint research unit (UMR 5516) of the Jean Monnet University, Saint-Étienne, the National Research Centre "CNRS," and the Institut d’Optique Graduate School. It is composed of about 90 researchers, professors, and assistant professors, 20 engineers and administrative staff, and 130 PhD and post-PhD students. This makes the Hubert Curien laboratory with a total of about 240 staff the most important research structure of Saint-Étienne. More information at https://laboratoirehubertcurien.univ-st-etienne.fr.


[1] J. Park, M. Kang, and B. Han, "Class-Incremental Learning for Action Recognition in Videos," ICCV 2021.

[2] M. Raissi, P. Perdikaris, and G. Karniadakis, "Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear PDE," Journal of Computational Physics 2019.

[3] M. Buisson-Fenet, V. Morgenthaler, S. Trimpe, and F. Di Meglio, "Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs," TMLR 2023.

[4] J. Bruna and S. Mallat, "Invariant scattering convolution networks," IEEE TPAMI 2013.

[5] A. Dekhovich, M. Sluiter, D. Tax, and M. Bessa, "iPINNs: Incremental learning for Physics-informed neural networks," preprint ArXiv 2023.


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