780 English-speaking jobs in Occitania

This project aims to improve river flood modeling by combining data from the SWOT and CO3D missions. Researchers will use CO3D's high-resolution topography to refine SWOT-derived bathymetry, particularly in narrow and complex river reaches. The project will also investigate the potential of CO3D for surface velocity retrieval and discharge estimation.

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.

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 research aims to improve the reliability of antenna measurements by developing new methods for assessing far-field patterns from raw data and quantifying uncorrectable uncertainties. The research will explore narrow-band delay-based models, wavelet-based deconvolution, and stochastic methods to address challenges related to spurious scatterings and environmental randomness.

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 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 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.

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 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 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 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 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 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 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 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 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 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.

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 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 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.