479 English-speaking jobs in Occitania

Provides executional and analytical support to accelerate eCommerce performance across global markets. Collaborates cross-functionally to ensure best-in-class digital shelf execution, eJoint Business Plans, and Retail Media optimization. Supports the eCommerce and digital acceleration of Dexeryl / Dexeclear brands in select markets.

This research position focuses on bridging the gap between two Earth observation models: TAMRF and RELEO. The work will extend the TAMRF architecture to a fully attention-based system using Perceiver-IO, supporting additional sensors and incorporating exogenous variables. The researcher will also contribute to RELEO's training strategy and architecture validation.

This PhD thesis focuses on developing advanced image processing methods for space coronagraphs, specifically addressing systematics limiting JWST and Roman observations. The student will explore analytical approaches using JWST wavefront measurements and existing coronagraphic images, and develop hybrid methods combining optical modeling with telemetry and archive data. The goal is to improve exoplanet detection limits by removing starlight contamination and instrumental artifacts.

This PhD research aims to develop a method for characterizing aerosol plumes from satellite data by jointly inverting plume and surface reflectance properties. The project will explore the use of variational autoencoders (VAEs) to provide prior information on surface reflectance, overcoming limitations of existing methods that rely on simplifying assumptions and plume-free images.

This research project analyzes lightning activity observed by the MTG-LI instrument, focusing on flash and storm scales. The study will compare lightning properties and cloud characteristics over Europe, Africa, and the Atlantic Ocean.

This kind of interference may be responsible for high execution time variability, and consequently, be the cause for a loss in execution determinism and performance. This problem has since become a concern in the safety-critical real-time community where assuring a task finishes within its deadlines is imperative.

This PhD project develops physics-constrained deep learning algorithms for BIOMASS P-band SAR tomography applications. The research integrates electromagnetic scattering principles into neural network architectures to achieve accurate forest structure reconstruction.

Develop a machine learning model to predict the in-flight response of a multi-head particle detector based on ground calibration data. The model will account for instrumental effects and background noise, enabling systematic calibration and intercalibration between detectors.

This research project focuses on developing a multi-sensor fusion approach to estimate bathymetry and associated uncertainty. The project will integrate existing methods, develop new agile methods for various sensors, and incorporate uncertainty estimation for fusion. The goal is to create a new global atlas of bathymetric data.

This PhD thesis investigates the use of remote sensing data to improve hydrological modeling. The research will focus on optimizing a semi-distributed GR model using various remote sensing products, including snow cover, soil moisture, evapotranspiration, and water levels. The goal is to enhance model parameterization, improve consistency between simulated variables and observations, and increase the model's spatial and temporal transferability.

This PhD research aims to develop a method for predicting the fatigue life of metal parts produced by L-PBF additive manufacturing, considering the influence of defects. The research will involve developing a two-scale approach linking a local model detailing defects to a large-scale model of the assembled structure.

This thesis focuses on developing a reduced-order model for the Salammbô 3D electrons code, used to simulate Earth's radiation belts. The research will explore dimensionality reduction, dynamic modeling in latent space, and the impact of dimensionality reduction on model accuracy.

This PhD research aims to characterize the radar cross section (RCS) of electric thruster plasma plumes. The work will involve numerical simulation using a multi-physics tool, development of advanced signal processing techniques for RCS measurement in reverberant environments, and experimental characterization in both anechoic and reverberant chambers.

This PhD research focuses on understanding transient X-ray events from compact objects like black holes and neutron stars. The student will analyze data from the XMM-Newton observatory and other telescopes to study phenomena such as X-ray binaries, cataclysmic variables, and tidal disruption events. The goal is to improve our understanding of accretion processes, the growth of supermassive black holes, and the evolution of compact objects.

This PhD analyzes the dynamics of internal tides (ITs), focusing on high modes and solitons, and their interactions with mesoscale circulation. The goal is to develop an AI-based method for automatic IT detection in SWOT and ocean color data to improve chlorophyll mapping.

This PhD thesis develops a hybrid navigation pipeline combining visual/inertial SLAM, physics- and geometry-grounded Vision Foundation Models, and 3D reconstruction techniques. The project aims to create robust navigation methods for complex environments, validated in simulations and on real robotic platforms.

This thesis focuses on improving numerical simulations of gas flows in hybrid rocket engine combustion chambers. The work involves integrating a pyrolysis law for solid fuel degradation into the MoDeTheC code and coupling it with the CEDRE code to simulate mass and heat transfer at the fluid/fuel interface. Simulations will be validated against experimental data from ONERA and used to optimize combustion chamber geometries for improved engine performance.

This research explores the potential of silicon-based front-end circuits operating at 183 GHz and 325 GHz for miniaturized space radiometers. The study aims to address challenges such as lower electron mobility and substrate losses in silicon technologies compared to III-V semiconductors.

This thesis develops a probabilistic framework for collision risk assessment in space, addressing uncertainties in covariance matrices, Time of Closest Approach, and nonlinear dynamics.

This research investigates the neural and cognitive adaptations to hypobaric environments simulating future lunar habitats. Using neuroimaging and physiological monitoring, the study examines the effects of reduced ambient pressure on brain function, autonomic regulation, and cognitive performance during simulated lunar habitat tasks.