Our team and mission

The Radio Frequency Payloads and Technology Division is responsible for RF payloads and technologies for space applications and associated laboratory facilities. More specifically TEC-EF responsibilities encompass at subsystem and instrument level:

• Payloads with RF interface exploiting different technologies (e.g., analogue, digital, optical) including design and performance analysis tools and testing;
• RF active and passive instruments, including design and performance analysis, engineering and testing up to sub-millimetre waves;
• Telemetry, tracking and control (TT&C) subsystems, payload data transmission (PDT) subsystems including deep space and near Earth transponders, proximity and intersatellite (ISL) link antennas and equipment, high speed downlink modulators, digital communication equipment;
• Wave-propagation and interaction, including signal impairments and regulatory aspects;
• Performance assessment of RF and Optical remote sensing data products (including spurious effects mitigation and calibration techniques) at Level-1 and Level-2, and associated data processing techniques;
• Antenna systems, architecture, technologies, and techniques for all space applications, including space vehicle TT&C and user segment terminals, sub- millimetre wave instruments and associated technologies, as well as antenna engineering, and RF testing of antenna and materials;
• RF technologies and RF equipment, also including vacuum electronics and high-power RF phenomena (multipactor, corona and passive intermodulation);
• RF Payload digital equipment, and on-board Payload Signal and Data Processing algorithms and techniques for RF payloads and instruments in close collaboration with TEC-ED; and
• Time and frequency references, modelling, design tools, measurements, performance characterisation and calibration techniques.

The Wave Interaction and Propagation Section provides functional support to ESA projects and carries out technological research (R&D) in the fields of wave propagation relevant to space communications, navigation and remote sensing, wave interaction for remote sensing of the Earth and other planets, in-situ characterisation of the interaction/propagation environment, data processing and data science techniques, and simulation/performance evaluation tools.

Candidates interested are encouraged to visit the ESA website: http://www.esa.int  

Field(s) of activity for the internship

Topic of the internship: Enhanced Retrieval Algorithms for the HydroGNSS Mission

The HydroGNSS Mission (www.hydrognss.org) is the first ESA Scout Mission, with a launch planned in Q4 2025. It is a constellation of two satellites phased apart by 180°. Each satellite carries onboard a GNSS-R (Global Navigation Satellite System-Reflectometry) receiver, to acquire the reflections of navigation signals from the Earth surface, and infer crucial bio-geophysical properties of the surface itself. HydroGNSS focuses on land applications, and it provides on-board Level 1 (L1) processed reflections (Delay Doppler Maps, DDMs), which are then inverted on the ground into hydrological products related to Essential Climate Variables (ECVs) from the Global Climate Observing System (GCOS). HydroGNSS will provide Level 2 (L2) observations of Soil Moisture, Surface Inundation, Freeze/Thaw cycle and Above Ground Biomass (AGB) as its primary objectives, while also delivering secondary products of ocean wind speed and sea ice extent. 

The baseline algorithms designed to estimate such variables from the observed data at Level 1 (L1) are based on Machine Learning (with the exception of that for Freeze/Thaw estimation). They have however been developed and tested using mostly simulated HydroGNSS data, or data from past GNSS-R missions, and as such they may be sub-optimal when applied to real HydroGNSS data. The commissioning phase of HydroGNSS is expected to end within Q2 2026, and the availability of a fairly large data collection from HydroGNSS in 2026 opens up possibilities to improve and enhance the baseline L2 algorithms developed for the mission.

The aim of this project is to develop and test enhanced L2 algorithms for the four hydrological parameters of HydroGNSS, leveraging a combination of machine learning techniques with real data from the mission. The improvements are expected to come from the optimisation of inputs and algorithmic parameters, as well as the full exploitation of the innovative measurements of the mission (dual-polarization reflection, coherent channel, and a second frequency). The enhanced performance of the novel algorithms will be evaluated and compared to the baseline approaches.

This project will have you assessing the capabilities of novel spaceborne instrumentation, analysing the physical relationship between instrument observations and bio-geophysical variables, and investigating the benefits and limitations of machine learning techniques applied to GNSS-R observations. Through this project, you are expected to gain knowledge and experience in these three areas as well as understanding on how satellite observations are converted into actionable products.

Behavioural competencies

Result Orientation
Operational Efficiency
Fostering Cooperation
Relationship Management
Continuous Improvement
Forward Thinking

For more information, please refer to ESA Core Behavioural Competencies guidebook

Education

You must be a university student, preferably studying at master’s level. In addition, you must be able to prove that you will be enrolled at your University for the entire duration of the internship.

Additional requirements

The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another ESA Member State language is an asset.

During the interview, your motivation for applying to this role will be explored.

You should also have:

• Good knowledge of Machine Learning;
• Good knowledge of Signal Processing, Data Analysis, Probability and Statistics;
• Prior Experience in high-level Programming languages such as Python and MATLAB.