Tenure track in Multimodal language processing


Closing date: 2 May 2022

Description of the research project

The research project should take place in the LST team goal that aims at developping a multimodal and multilingual representation space for speech and text modalities. The Junior Professor is expected to develop his/her own research diretions between the topics already existing in the LST team and to develop hybrid approaches by mixing for instance speaker characterization and speech synthesis or speech translation and speech understanding. He/She should also integrate the strategy of the team to involve the human in the loop for deep learning systems and work towards a better explainability/interpretability of speech processing algorithms.

Description of the teaching project

Teaching activities will take place within the Master of Computer Sciences and Artificial Intelligence from Le Mans University. The candidate is expected to strengthen the teaching on deep learning (self-supervised training, GANs, Transformers, machine learning methodology and protocols…) but also teach tools for distributed learning (SLURM, MPI, ssh, temux, jupyter-lab, conda…) and cloud computing. In mid terms, the candidate will contribute to the development of a continuing learning in artificial intelligence adapted to the need of local companies and industry but also for researchers non-specialist in computer sciences.

Application requirements

  • hold a PhD
  • application on Galaxie
  • For candidates exercising or having ceased to exercise for less than eighteen months a function of teacher- researcher, of a level equivalent to that of the position to be filled, in a higher education establishment of a State other than France: titles, works and any element allowing to appreciate the level of function allowing to grant a dispence of doctorate.


Antoine LAURENT : Antoine.laurent(at)univ-lemans.fr
Anthony LARCHER : Anthony.larcher(at)univ-lemans.fr
Le Mans Université
Avenue Olivier Messiaen 72085 LE MANS CEDEX 9
Tel: 02 43 83 30 00