496 English-speaking jobs in Toulouse

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 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.

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 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.

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 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.

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 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.

This PhD study investigates the impact of high-energy radiation on spacecraft components and characterizes electrostatic discharges on critical elements. The research involves experimental, instrumental, and numerical methods to identify and measure discharges under simulated space conditions.

This thesis investigates the impact of UV radiation on contaminant morphology through photofixation. The research aims to identify the chemical species involved in UV-contaminant interactions and establish a protocol to characterize these reactions.

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 thesis develops AI tools to optimize plant cultivation in closed-loop life support systems for space missions. It focuses on using machine learning and computer vision to estimate plant and environmental states, and reinforcement learning to compute autonomous cultivation strategies.

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.

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

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 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 research aims to develop a numerical model predicting the transition from multipactor to RF discharge inception in high-frequency and radio-frequency payloads. The work will investigate electron avalanche processes, gas ionization dynamics, and the influence of geometry and material properties.

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.