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 RF Payload Architecture Section provides functional support to ESA projects in all the application directorates and carries out technological research (R&D) in the field(s) of telecom/navigation/earth observation payload architecture, telemetry, tracking and control subsystems, RF digital equipment for telecom/navigation/earth observation payloads, including digital signal processing and associated technologies.

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

Field(s) of activity for the internship

You will have the opportunity to contribute in one of the following disciplines, depending on your background and interests.

You can choose between the following topics:

1) Topic 1: Remote Sensing Digital Back-end using European Technology
The objective of this topic is to survey the state-of-the-art of Electrical, Electronic and Electro-Mechanical (EEE) components to be used in the implementation of Remote Sensing Digital Back-ends, focusing on European suppliers, and critically assess their capabilities and gaps, through analysis and/or prototyping of relevant functions in hardware, for a given architecture. The EEE components to be considered shall encompass data conversion (e.g. Analogue-to-Digital Converter, Digital-to-Analogue Converter, Integrated Radio Frequency Transceivers), digital processing (e.g. Field-Programmable Gate Array, System-on-a-Chip, Coarse-Grained Reconfigurable Array, etc.) and clock synthesis and distribution (e.g. Phase-Locked Loop, clock buffers, etc.). The type of functions to be considered shall be relevant to future remote sensing digital back-ends, such as direct conversion, multi-channel coherent acquisition and/or digital processing.

2) Topic 2: Modelling of Telecom On-board Processing
The objective of this topic is to model a telecommunications on-board processor (transparent and/or regenerative) at block level, to be used in performance analysis, e.g. power consumption, heat dissipation, etc. The tasks envisaged encompass the survey of on-board processor architecture, identification of major blocks and interfaces, definition of a modelling architecture, modelling of blocks and implementation of use-cases for model validation, including relevant waveforms. Correlation with hardware may also be included.

3) Topic 3: Payload Architecture with Active Antennas and Distributed Digital Beam-forming
The objective of this topic is to address the architecture of active antennas with close-to-aperture data conversion and exploiting distributed beam-forming, which can be scaled up to thousands of elements. Key challenges to be tackled are the interfaces between active antenna and payload back-end (digital data, clock distribution synchronization) as well as power distribution and dissipation.

4) Topic 4: On-board Digital Signal Processing and Machine Learning/Artificial Intelligence
The objective of this topic is to model and implement functions commonly used in on-board processing, assessing their performance. The modelling and implementation shall exploit the latest paradigms in programming and modelling languages, such as Python, Matlab/Simulink, etc. Another option is to exploit Machine Learning/Artificial Intelligence techniques, for functions such as RF signal identification, and in this case the tasks shall encompass the definition of a relevant data set, explore adequate models, training of the chosen model(s) and evaluation.

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.

Knowledge and background in one of the following disciplines shall be considered an asset:

• Analog and digital signal processing and/or radio frequency and digital sub-systems;
• On-board processing;
• Machine Learning and AI;
• Knowledge and/or experience in computer programming (e.g. Python, Matlab/Simulink).