Our team and mission

The Radio Frequency Payloads & Technology Division (TEC-EF) is responsible from early definition to full qualification of RF payloads, TT&C subsystems, RF instruments and technologies for space and ground applications, including all equipment having a Radio Frequency space/ground interface and its associated Laboratories.

The division consists of four sections covering the following domains:

• 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);
• 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 equipment and technologies, including RF passive technologies, RF active technologies, vacuum electronics and high power RF phenomena (multipactor, corona, power handling 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;
• time and frequency generation and distribution equipment (e.g. microwave and optical with RF interfaces), techniques, performance tools, measurements and characterisation.

Within the TEC-EFE section, the team works on Time and Frequency (T&F) technologies: oscillators, atomic clocks, clock monitoring units or UTC laboratory tools for time realisation and time transfer require extensive characterisation and performance analysis under a wide range of operational and environmental conditions. 

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

Field(s) of activity for the internship

You can choose among the following topics:

1) Topic 1: AI-Assisted Analysis of Compact Clocks

You will contribute to the analysis and characterisation of space atomic clocks, with a particular focus on the investigation of compact clocks anomalies, including frequency jumps and their correlation with housekeeping telemetry parameters. Working with real measurement data obtained from long-term laboratory and environmental test campaigns, you will analyse clock performance and compare the observed behaviour against existing in-house clock models.

The activities will include post-processing and visualisation of time and frequency data from both conventional space-qualified atomic clocks and emerging low Size, Weight and Power (low-SWaP) timing technologies. You will identify trends, recurring patterns, frequency excursions and deviations from the expected clock behaviour, while assessing potential correlations with operational and environmental factors.

The internship will also provide exposure to state-of-the-art techniques for atomic clock monitoring and performance assessment. You will investigate and evaluate the applicability of data-driven approaches, including machine learning methods, for anomaly detection, classification and prediction. Particular emphasis will be placed on the development of tools and methodologies capable of supporting the automated analysis of large telemetry and frequency datasets.

Through this work, you will gain hands-on experience in space timing technologies, signal processing, statistical analysis, data analytics and machine learning, while contributing to activities relevant to current and future navigation, telecommunications and scientific missions relying on highly stable frequency references.

2) Topic 2: AI aided design for THz applications

The design of millimetre and sub-millimetre wave receivers featuring Schottky diode devices as non-linear elements for mixers and multipliers is traditionally done via a combination of 3D Electro-Magnetic (EM) simulations and linear/non-linear circuit simulations with harmonic-balanced capabilities. Software EM/circuit suites usually integrate both types of solvers with direct interaction between modules, but the optimisation and circuit topology including waveguide cavities, active device modelling and packaging rely heavily on the designer's experience. In that respect, the final circuit design often looks like the previous ones with only minor modifications.

Your objective for this internship is to train an Artificial Intelligence (AI) using existing designs successfully demonstrated in the past and recently published designs from peer-reviewed literature, in order to improve the performance of combined building blocks (mixer, multiplier) in a co-design approach at the higher integration level for best system performance. You will investigate the use of AI to explore novel circuit topologies with improved waveguide coupling, better non-linear device models, and possibly coupling to a thermal software package.

This work will greatly benefit potential future integration and performance optimisation of radiometer instruments on-board future meteorological missions (i.e. next generation of MetOp-SG, AWS (sub)-millimetre-wave payload instruments, EE12 Keystone THz spectrometer candidate missions, …).

Additionally, the new design methodology based on AI could be extended in the future to any transmit/receiver development at higher frequencies (D-band and above) where the packaging of active devices (transistors, diodes, …) plays a key role in the optimisation of the performance.

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 in your final or second-to-last year of university course 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 signal processing, data analysis, probability and statistics;
• prior experience in high-level programming languages such as Python and MATLAB.

The following will be considered assets:

• knowledge of time-series analysis and measurement data processing;
• experience with scientific computing and data visualisation tools;
• familiarity with measurement systems, instrumentation or test automation;
• knowledge of Machine Learning techniques.