126 English-speaking jobs in Toulouse

A PhD student will develop experimental protocols to measure the electrical properties of lunar dust simulants in a simulated lunar environment. The research will focus on the impact of dust grain electrical properties on charge and adhesion under varying temperatures and irradiation.

Regulatory CMC Development Expert needed for permanent position in Toulouse or Boulogne. Develop CMC regulatory strategy for assigned projects from early clinical trials to marketing applications. Collaborate with Pharmaceutical Development and Regulatory Development Teams.

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 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 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.

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 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.

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

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 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.

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.

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 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 PhD research develops robust models of four-wheeled rovers for dynamic behavior, aiming to increase cruising speed to five meters per second. The research integrates structural characteristics, damping, control, and interface capabilities with emerging technologies.

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 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 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 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.