Job offer

European Commission; France; Sur place; EUR 35 000 - 45 000; Plein temps

Résumé du poste

Le Laboratoire de Physique cherche un post-doc pour développer des méthodes d'analyse des signaux bioacoustiques. Le candidat participera à des recherches avancées sur des modèles de représentation et contribuera à des publications scientifiques dans un environnement collaboratif. Des compétences en traitement du signal et en apprentissage automatique sont essentielles.

Qualifications

• Solide expérience en traitement du signal et/ou apprentissage automatique.
• Compétence en programmation scientifique (Python de préférence).
• Intérêt pour les représentations temps-fréquence/échelle.

Responsabilités

• Développer des modèles de représentation basés sur l'apprentissage profond.
• Évaluer le modèle sur des bases de données réelles.
• Contribuer à des publications scientifiques et participer à des conférences.

Connaissances

Traitement du signal

Apprentissage automatique

Programmation scientifique

Formation

Doctorat en traitement du signal ou domaine connexe

Description du poste

Organisation/Company CNRS Department Laboratoire de Physique Research Field Engineering Computer science Mathematics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 7 Jul 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The post-doc recruited will participate in the development of new representations for the analysis of non-stationary signals, in particular bioacoustic signals. The main objective is to design, implement and evaluate a constrained neural architecture capable of producing interpretable and compact representations, based on the broken stationarity model. This work is part of a wider effort to gain a fine-grained understanding of complex acoustic signals for analysis or classification purposes.

- Development of a representation model based on deep learning, constrained by physical and mathematical considerations (spectrum of a stationary signal + deformation function).

- Implementation of the model in the form of a non-linear neural network, with a decoder reproducing the synthesis operation.

- Quantitative evaluation of the model on real databases, in particular the Watkins Marine Mammal Sound Database.

- Performance comparison with conventional time-frequency analysis methods (wavelets, Fourier).

- Contribution to scientific publications and participation in international conferences.

- Interaction with researchers in machine learning and bioacoustics to foster interdisciplinary synergies.

The successful candidate will join the Signals, Systems and Physics (Sisyph) team of the Laboratoire de Physique at ENS Lyon. This team produces high-level research for methodological development in the field of information processing (from signal and image processing to machine learning).

- Solid background in signal processing and/or machine learning.
- Good level of scientific programming (Python preferred).
- Knowledge of or interest in time-frequency/time-scale representations.
- Ability to work independently in a collaborative environment.
- Taste for numerical experimentation, reproducibility and critical analysis of results.