Young Graduate Trainee for Data Science on Archives

Within the Operations Department’s Data & Engineering Division, the ESAC Science Data Centre (ESDC, http://cosmos.esa.int/web/esdc/) is responsible for developing and operating the science data archives of all ESA space science missions (astronomy, solar system) in close cooperation with the Science Operations Centres at ESAC, the instrument teams and the scientific community.

We are developing a set of data science applications to exponentially expand the scientific exploitation of the data sets by applying artificial intelligence techniques. Successful pilot studies included the training of deep Convolutional Neural Networks (CNN) to identify asteroid trails in Hubble Space Telescope images or interstellar medium bubbles in Herschel images with training samples provided by citizen science projects. Based on these successful pilot programmes, the one-year goal of the project is to have all image contents of ESA’s astronomical archives classified automatically by one or more CNNs to allow users to search them for science content on the images (e.g. asteroid trails, ISM bubbles, elliptical, spiral, spiral barred and irregular galaxies, nebulae and single or multiple stars) – instead of just on the meta data of the images, as all other astronomical archives worldwide allow today. This new feature will truly enable higher-level science exploitation by astronomers and data scientists alike, boosting the science return on ESA science missions.

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

As a YGT you will work with a small team of software engineers and scientists at ESAC. The outcome of your work will be part of the future operational version of the ESASky portal to all astronomy data, for which you will be given due credit. You will get all needed software and computer support and training for the project. An Agile/Scrum software development methodology will be used.

You should have just completed, or be in  the final year of a university course at Master’s level (or equivalent) in a technical or scientific discipline, with demonstrable experience in Machine Learning and Data Science.

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.

You should demonstrate good interpersonal skills and the capacity to work both independently and as part of a team.

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