Our team and mission

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

Under the direct authority of the Directorate of Science, the Head of the Science Operations Department is responsible for the development of the science operations infrastructure under the Directorate responsibility, the operation of the Directorate’s missions once successfully commissioned, and the curation of all scientific data in the missions’ legacy phase. These responsibilities are discharged in full coordination with the Directorate’s Departments and Offices and as appropriate, with the Directorate of Operations (D/OPS).

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

• Mission Management and Science Operations Division (SCI-SO);
• Science Operations Development Division (SCI-SD);
• Data Science and Archives Division (SCI-SA).

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

Field(s) of activity for the internship

Topic of the internship: Searching For Anomalous Galaxies in Science Archives with Machine Learning

Galaxies host an assortment of strange morphological features depending on their evolutionary history. These can range from the formation of tidal features in galaxy interaction, the formation of bars or spiral arms due to secular processes, or the complete removal of gas in dense environments. Thus, by finding galaxies with eccentric and unique morphologies, we can probe at the most extreme processes which drive galaxy evolution.

An example of an anomalous galactic system with an extreme morphology is the jellyfish galaxy. These systems reside in the densest of galactic environments, where the intergalactic medium acts to ram pressure strip the gas out of the galaxy. As the gas is removed, long tendrils form giving the system the unique morphology that led to its name. The removal of gas has disastrous consequences for the galaxy, rapidly quenching it and leaving it as a ‘red and dead’ galaxy where no further stars form.

Due to the unique and extreme environments of these anomalous systems, we have observed relatively few of them. This, in turn, means they are difficult to study with any statistical significance. We propose to use a machine learning algorithm, specifically developed to classify galaxy morphology, to discover more of these systems in the Euclid science archives. We will create image cutouts of many sources from Euclid and use our machine learning algorithm to classify their morphology. We will use the novel power of the ESA Datalabs platform to achieve this. This platform will provide us with direct access to Euclid’s science archives and allow us to efficiently explore Terabytes of observational data and create millions of source cutouts for classification.

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 a university course at Master’s level and you need to remain 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 Member State language would be an asset.

• Experience with the Python programming language;
• Experience with Anaconda (or equivalent) and JupyterLab;
• Experience with using and deploying machine learning algorithms;
• Experience with handling large astronomical datasets and imaging;
• Knowledge of extragalactic astronomy is desired, but not essential;
• Knowledge of CUDA and GPU programming is desired, but not essential.