PhD offer (starting date: Fall 2026) Context and Objectives: Near-field acoustic holography is an imaging technique based on the measurement of the acoustic field using a microphone array. It enables the reconstruction of acoustic quantities (pressure, particle velocity, intensity) in the vicinity of sound sources, providing a precise spatial and…
Domaine : Science de Données – Mathématiques Appliquées / Mines Saint-Etienne – Institut Henri Fayol – Département Génie Mathématique et Industriel (GMI).LIMOS (UMR CNRS 6158) Programme Futurs , Ruptures et Impacts : rentrée Octobre 2026
[Thèse CIFRE STM/Telecom Paris] Méthodes avancées de mitigation d’interférences pour un Radar FMCW [Thèse CIFRE STM/Telecom Paris] Méthodes avancées de mitigation d’interférences pour un Radar FMCW
Application deadline : April 18, 2026 Final results : June, 2026 Duration : 3 years starting from October 2026 Place : Université de Lille – CRIStAL, Villeneuve d’Ascq 59655, France Supervisors: • Mohamed Daoudi, Professor (mohamed.daoudi@univ-lille.fr) • Deise Santana Maia, Assistant Professor (deise.santanamaia@univ-lille.fr) This PhD thesis proposal falls within the scope of the research…
★ The internship will take place within the FOX team of CRIStAL Laboratory, of University of Lille. Advantage : This is primarily a PhD offer, but it is possible to start with Masters-2 internship (however, it is not at all compulsory). Resume : The project aims to design a neuromorphic…
★ The internship will take place within the FOX team of the CRIStAL laboratory at the University of Lille. Advantage: This internship is supposed to be continued with a PhD thesis upon completion Project Description : The project aims to design a neuromorphic system capable of detecting spatio-temporal patterns in…
Starting date: October 2026Application deadline date: March 20th, 2026Final decision date: June 4th, 2026Contact: hugues.patural@univ-st-etienne.fr, olivier.alata@univ-st-etienne.fr Position Overview This PhD project corresponds to a fully funded 3-year doctoral position within the framework of the Doctoral School EDSIS. The thesis will be conducted at Université Jean Monnet, Saint-Étienne, France, with an…
Mots clés : Estimation robuste du signal MT, deep Learning interprétable, traitement adaptatifen contexte bruité, séries temporelles multivariées, suivi électromagnétique des volcans Résumé : L’exploitation des données magnétotelluriques (MT) en contexte fortement bruité reste un défi scientifique majeur, en particulier pour la détection de signaux faibles associés aux dynamiques profondes…
The foundation model (FM) paradigm is undoubtedly a major breakthrough in Machine Learning (ML) for Artificial Intelligence (AI). An FM is a large-scale neural network pre-trained with self-supervision on a vast unannotated dataset and designed to perform downstream tasks with minimal fine-tuning on small annotated datasets. While FMs have made…
