731 English-speaking jobs in Occitania

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 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 PhD research focuses on developing a new type of powder damper for mitigating micro-vibrations in satellites. The project will involve developing a dynamic model, characterizing the damper through vibration tests, and optimizing its design for maximum energy dissipation.

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 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 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.

This doctoral project focuses on designing robust attitude control laws for balloon-borne pointing systems. The work will involve developing control techniques that address system uncertainties and actuator limitations, including online adaptation strategies and control allocation schemes. Validation will include robustness analysis and experimental testing on a CNES test-bench.

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 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 project aims to design a next-generation attitude simulator testbed capable of assessing attitude control algorithms under realistic conditions. The new design will eliminate disturbances not found in orbit and handle active dynamic imbalances at the payload level. The project will investigate the use of miniaturised Control Moment Gyroscopes (CMGs) for agile balancing algorithms, decoupling an agile balancing system from the payload's ADCS, and optimizing nonlinear multibody simulations for prototyping and validating control algorithms.

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.

PhD research focused on understanding and identifying defects in irradiated silicon detectors that cause Dark Current Random Telegraph Signal (DC-RTS), leading to image degradation in space applications.

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 research position focuses on improving the accuracy and capabilities of DTM_nrt, an AI-based thermosphere density forecast model. The successful candidate will investigate model limitations, explore data assimilation techniques, and develop enhancements to the model's performance.

CNES is developing a sustainable water recycling system for space missions using photocatalytic degradation and in situ reactive oxygen nitrogen species (RONS) generation. The system integrates iron oxide nanoparticles and plasma-generated hydrogen peroxide to degrade organic and inorganic pollutants in wastewater.

This PhD project investigates the impact of blue economy interventions (marine protected areas, mariculture, eco-tourism) on poverty and outmigration in coastal villages of Madagascar. Using satellite imagery, AI algorithms, and statistical methods, the research will assess the causal link between blue economy development and socioeconomic outcomes over a 20-year period.

This PhD research focuses on developing and validating numerical models to study the effects of water injection on the acoustic and thermal environment during rocket launch. The work will involve multi-phase flow modeling, acoustic and thermal simulations, and comparisons with experimental data from the MARTEL test bench.

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 PhD research will investigate the microphysics of triboelectric charging of dust grains on Mars. The study will combine experimental, theoretical, and numerical approaches to understand how charge is exchanged during grain-grain collisions and how ambient conditions influence these exchanges. Results will be compared with in situ data from the SuperCam instrument on the Perseverance rover.