2 years Post-doc position in Explainable AI and Evolutive Architecture for Neural Networks

The Speech and Language Technology Group in Le Mans University is looking for a post-doc scientist to work on evolutive end-to-end neural networks for speaker recognition
Keywords: Deep Learning, evolutive networks, genetic algorithms, explainability, speaker recognition, end-to-end
Context
The LST team from LIUM (Le Mans University) is focusing on evolutive end-to-end neural networks for speaker recognition. The Extensor project (French ANR funded) aims at developing novel architectures for end-to-end speaker recognition as well as explaining the behavior of those networks. The focus of Extensor is threefold:
get rid of the legacy of bayesian system’s architecture and explore wider opportunities offered in deep learning;
explore real end-to-end architectures exploiting the tax signal instead of classical features (such as MFCC of filterbanks);
Develop tools for explainability in speaker recognition.
Missions
Develop end-to-end speaker recognition system based on state-of-the-art approaches (x-vectors, sincnet…)
Develop evolutive architectures making use of existing genetic algorithms and study their behavior.
Participate to the three hackathons organized by the Extensor project in order to develop tools for evolutive neural network architecture and explainability for speaker recognition.
Dissemination: the research will be published in the major conferences and journals
Duration: 24 months
Salary: 2 600€ (after taxes)
Start: as soon as possible, latest June 2020
Location: LIUM, Le Mans University
Supervisers: Anthony Larcher (anthony.larcher-AT-univ-lemans.fr)
Expected competences:
- Phd in Machine Learning and Deep Learning
- Experience in speech processing is positive
- Python fluent
- familiar with a deep learning toolkit (Pytorch, TensorFlow)