Young Graduate Trainee in Machine Learning Classification Tools (Gaia Archive)

Young Graduate Opportunity in the Directorate of Science

ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. We therefore welcome applications from all qualified candidates irrespective of gender, sexual orientation, ethnicity, beliefs, age, disability or other characteristics. Applications from women are encouraged.

This post is classified F1 on the Coordinated Organisations’ salary scale .

Location

ESAC, Villanueva de la Cañada, Spain 

ESA maintains a world-leading Science Programme with missions in heliophysics, planetary science, astrophysics and fundamental physics. Its mission is to 'Empower Europe to lead space science'. The Department for Science and Operations hosts the scientists and engineers that oversee the space missions, from study to end of operations. It develops the science operation systems for the missions and operates the missions in space, as well as archiving and curating their data during operations and beyond. Our main objective is to maximise the scientific output of the missions for the benefit of humankind.

You will work in the Gaia Science Operation Centre, in close collaboration with the ESAC Science Data Centre (ESDC), whose purpose includes promoting reliable space science data from the entire suite of ESA’s space science missions.

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

Gaia is a European Space Agency (ESA) cornerstone mission that is producing one of the most comprehensive astronomy catalogues ever built to date. The different products generated by this mission include distances, positions and velocities, astrophysical parameters, and photometric measurements for more than one billion sources. This vast dataset is publicly accessible via the Gaia ESA Archive, which roughly receives one request per second (on average) and has the potential to become one of the main entry points for every astronomer aiming to find useful information about a celestial object.

Combining the information provided by Gaia with complementary datasets can boost (even more) the usability of the Gaia products. The team that supports the Gaia Archive has started a project to develop a tool that combines the photometry measured by Gaia with the photometry compiled by other observatories (like 2MASS and WISE) and generates the Spectral Energy Distribution (SED) of the different sources observed by Gaia. The SED is a unique curve that contains the characteristic fingerprint of astrophysical phenomena such as accretion discs or dust envelopes, and it allows to classify stellar objects in different families (like young stellar objects, main sequence, or evolved stars). The Gaia Archive contains enough data to build a set of SEDs with high quality photometry (from optical to near-infrared wavelength) for roughly 100,000 sources. This dataset represents an ideal input to train different machine learning algorithms to classify objects based on their SED.

Your aim will be to develop a tool to classify objects based on the shape of their SED using machine learning algorithms (e.g., random forest or neural networks). The ultimate goal of the project is to implement this attractive functionality in the Archive using an interactive tool or plugin, and make it publicly available via the Archive Web interface and/or the ESA Datalabs platform. During this project, you will:

• Learn Astronomical Data Query Language (ADQL), the language adopted by the Virtual Observatory to access astronomical datasets;
• Learn to use a Python-based tool to construct a set of Spectral Energy Distributions using the data hosted by the Gaia;
• ESA Archive;
• Identify suitable training subsets for object classification;
• Apply different machine learning algorithms to classify objects based on their SEDs;
• Apply different metrics (precision/recall, F1, etc) to identify the most suitable algorithm for this project;
• Develop and integrate this project in the ESA Datalabs framework.

In addition, you will have the opportunity to interact with the team of expert engineers and scientists who support the Gaia Archive and learn about the technical aspects behind the Archive.

This project gives the opportunity to make the Gaia ESA Archive the first astronomy archive offering an SED classification tool based on machine-learning algorithms.

Knowledge of relevant technical domains

Relevant experience gained during internships/project work

Breadth of exposure coming from past and/or current research/activities

Knowledge of ESA and its programmes/projects

Result Orientation

Operational Efficiency

Fostering Cooperation

Relationship Management

Continuous Improvement

Forward Thinking

You should have just completed, or be in the final year of your Master’ s degree in computer science, or similar.

You should have good interpersonal and communication skills and should be able to work in a multicultural environment, both independently and as part of a team.

Affinity with machine learning, Python, and structured query language (or astronomical data query language) is an asset, but the traineeship offers ample opportunities to improve skills and experiences in these domains. This project is oriented to candidates with a background in astronomy and astrophysics, but other backgrounds (like data science) will be considered as well.

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.

During the interview motivation and overall professional perspective/career goals will also be explored.

Other information

For behavioural competencies expected from ESA staff in general, please refer to the ESA Competency Framework .

For further information on the Young Graduate Programme please visit: Young Graduate Programme andFAQ Young Graduate Programme

At the Agency we value diversity and we welcome people with disabilities. Whenever possible, we seek to accommodate individuals with disabilities by providing the necessary support at the workplace. The Human Resources Department can also provide assistance during the recruitment process. If you would like to discuss this further please contact us email [email protected] .

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Please note that applications are only considered from nationals of one of the following States: Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden, Switzerland, and the United Kingdom. Nationals from Latvia, Lithuania, Slovakia and Slovenia, as  Associate Member States, or Canada as a Cooperating State, can apply as well as those from Bulgaria, Croatia and Cyprus as European Cooperating States (ECS).

According to the ESA Convention, the recruitment of staff must take into account an adequate distribution of posts among nationals of the ESA Member States*. When short-listing for an interview, priority will first be given to candidates from under-represented Member States *. 

In accordance with the European Space Agency’s security procedures and as part of the selection process, successful candidates will be required to undergo basic screening before appointment conducted by an external background screening service. 

*Member States, Associate Members or Cooperating States.