Intern in the Science Operations Department - Solar Orbiter App Development

Our team and mission

Under the direct authority of D/SCI, 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 their duties, SCI-S 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: Developing the Solar Orbiter App in ESA Datalabs

Solar Orbiter is an ESA-led flagship mission in heliophysics to observe the Sun and the heliosphere. The respective Solar Orbiter ARchive (SOAR) at ESAC contains more than 5 million unique science files measured by ten instruments aboard the spacecraft. These data include high-resolution remote-sensing observations - such as the closest images of the Sun ever taken and the world-first observations of the solar poles. In addition, the data contain state-of-the-art in-situ measurements of solar particles and the electromagnetic field at the spacecraft site, which allow phenomena on the Sun to be linked with their impact on the inner solar system. 

The goal of this project is to make key-science datasets from Solar Orbiter more accessible to the scientific community by developing a new application within the ESA Datalabs (EDL) environment. EDL serves as a primary computational platform for accessing and analysing ESA's astronomical datasets and is currently being expanded towards planetary and heliophysics datasets. One central aspect of EDL is that users can run their own code directly on the archived mission data volume instead of downloading all these data. Taking advantage of this infrastructure, we aim to integrate a comprehensive “starter package” for scientific data analysis within one single “Solar Orbiter App” in EDL. 

The scientific analysis code with examples for each Solar Orbiter instrument is currently available as individual Jupiter notebooks that demonstrate important steps for the analysis of key-datasets. You will work together with experts from ESA archives and Datalabs on the full implementation cycle of the application up to its public release, including the design of its architecture, the integration of code and access to the archive data, and finally the systematic testing of the application with implementation of necessary improvements. In the end, the Solar Orbiter App shall allow new users a kickstart for their science projects and for experienced users it shall demonstrate the added value of EDL by containing code applications based on clear scientific use cases to run on the mission data for one or several instruments datasets.

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:

• strong background in physics, computer science, or software engineering;


• proficiency in Python (at least 1 year's experience in scientific or software development projects);


• solid knowledge of astrophysics, space physics, or plasma physics (completed at least one university course on one of these topics).

Highly valued: 

• experience with cloud platforms, containerisation (Docker), and distributed computing;


• experience in scientific data analysis with Python (ideally space physics remote-sensing or in-situ data).