Our team and mission

This position is based at the European Space Astronomy Centre (ESAC) – Near Madrid, Spain

Under the direct authority of the Director of Science, the Science and Operations Department is responsible for ensuring that maximum scientific return, within applicable constraints, is maintained as an objective for the Directorate’s missions through their lifetime, by providing scientific oversight of the Directorate’s missions throughout their lifecycle, managing and being responsible for the operation of the Directorate’s missions once successfully commissioned, and curating the scientific data in their legacy phase, while establishing and maintaining the necessary science interfaces to the community. These responsibilities are discharged in full coordination with the Directorate’s Departments and Offices and, as appropriate, with the Directorate of Operations.

In implementing its duties, the Science and Operations Department is supported by the:

• Science Division
• Mission Operations Division
• Science Operations Development Division
• Data Science and Archive Division
• Mission Support Office

For further information visit our web site: http://www.esa.int

Field(s) of activity for the internship

You can choose between the following topics:

1) Topic 1: Ariel Exoplanet Mission Science Planning

Ariel is an upcoming ESA exoplanet mission (launching 2029), designed to perform a large (>1000) diverse survey of planetary atmospheres. The telescope will observe each target during transits and eclipses by the host star, allowing the recovery of faint signal from the planetary atmosphere from amongst the much stronger starlight. High signal-to-noise will be ensured through repeat observations for all planets during the nominal four year mission, each executed at the precise time of the transit or eclipse event.

Science planning for such a tightly constrained mission schedule is one of the most important and complicated tasks of the ESA Ariel Science Operations Centre (SOC) at ESAC. Fortunately, the Long Term Planning Tool for Ariel is already being developed, and this project focusses on exercising that tool under a range of potential mission scenarios. The goal is to explore the tool to ensure it is capable of effectively planning and replanning the mission timeline regardless of post-launch developments including, but not limited to, the injection of new high priority targets into the parent sample, extended operational downtime, or instrument anomalies leading to degraded data products. After familiarisation with the planning concept, you are free to investigate which scenarios are most critical for a realistic stress-test of the tool, and will have the opportunity to present their results to appropriate stakeholders. As well as providing Ariel with an independent verification of a critical tool, this project will offer you insight into the workings of ESA science operations.

For more information on this topic, please visit: Ariel Exoplanet Mission Science Planning

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2) Topic 2: CubeSat integration and performance validation of polarized antennas

Our team has been working on the design and building of a CubeSat, whose payload is a set of antennas providing polarized microwave signal to calibrate Cosmic Microwave Background ground-based experiments. The antennas are ready to be tested and the subsystems of the CubeSat ready to integrate. Your work will be to perform the integration and testing of the CubeSat platform, integrate the antennas, and define the tests to be performed with the ground station, flying the hardware on a balloon or drone over the QUIJOTE experiment in Tenerife.

The project will need that you get involved in all subsystems: communication, AOCS, EPS, structure, mechanisms and on the operational aspects both during the testing on Earth and the definition of the operations in space (orbit, power budget profile, visibility, etc).

For more information on this topic, please visit: CubeSat integration and performance validation of polarized antennas

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3) Topic 3: Deep Learning Techniques applied to SW quality assessment

The ESAC PA provides transversal support to all mission in the Science department. The large amount of data collected on the code quality from the different missions has reached a level where automation and other techniques are required to assess the overall quality of the software products.

Your work will consist in the analysis of the quality metrics to identify areas where Deep Learning can improve the quality assessment of source code, classification of errors, determination of common issues, testing trends, etc. Based on this analysis you will implement Deep Learning (i.e., deep neural networks) to perform one or more of the identified tasks as a demonstrator of the idea. The aim of your project is to derive conclusions and define the way forward on the application of Deep Learning and other Machine Learning techniques to improve source code quality.

Additionally, you will work with the PA team to identify the key aspects the Product Assurance activities applicable to Machine Learning development for scientific data analysis. Based on this, the development cycle should create a new PA approach suitable to this new paradigm comprising new mechanisms for verification and validation.

For more information on this topic, please visit: Deep Learning Techniques applied to SW quality assessment

Behavioural competencies

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

Education

You must have student status and be enrolled at university for the entire duration of the internship. You should preferably be in your final or second to last year of a university course at master’s level in a technical or scientific discipline.

Additional requirements

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

Additional Requirements:

1) Topic 1:

• Some experience with command line computing (e.g. terminal, Unix) and a scripting language (e.g. python) is beneficial

2) Topic 2:

• Knowledge of Space Engineering and satellite subsystems
• Knowledge of microprocessors and microcomputers as applied to CubeSats
• Knowledge of antennas and RF/EM in general
• Knowledge of testing processes and tools
• Knowledge of Matlab/Simulink

3) Topic 3:

• Knowledge of Software Engineering process
• Knowledge of Machine and Deep Learning algorithms
• Programming languages: Python, Java
• Knowledge of testing processes and tools
• Knowledge of CI/CD
• Knowledge of Matlab/Simulink
• Knowledge of SW quality assurance practices and techniques