Intern in the Science Engagement and Oversight Office, Automating Martian Terrain Classification

Our team and mission

Under the direct authority of D/SCI, the Head of the Science Engagement and Oversight Office is responsible for overseeing the scientific content of the Programme, interfacing with the scientific community and providing scientific expertise to studies, projects and missions, also in other Directorates as needed.

Field(s) of activity for the internship

Topic of the internship: From Pixels to Predictions: Exploring Machine Learning Techniques for Martian Terrain Characterization

The surface of Mars exhibits extensive evidence of spatially and temporally diverse exogenic processes. These range from centimetre-scale ripples that have been observed to change on the order of minutes to hours, to vast fluvial valley networks carved billions of years ago under a markedly different atmospheric and climatic regime. These features provide evidence for a dynamic planet, the history of which has dramatically diverged from that of our own.

While rovers and landers have provided invaluable insight into site-specific aeolian, fluvial, and geochemical changes to the landscape, our understanding of Mars has largely been derived from high-resolution remotely captured images of the surface. These have been acquired by a constellation of orbital platforms. In particular, High Resolution Imaging Science Experiment (HiRISE) images (up to 25 cm per pixel) are critical in characterizing potential landing sites for future robotic and human-led missions. By examining such images within Geographic Information Systems, we can learn about the processes which shape the Martian landscape.

A challenge with all this data is that it takes substantial resources, both in terms of human-led investigation and computational power, to characterize and classify diverse terrains. In this internship, we propose to build a lightweight, site agnostic terrain characterization algorithm, powered by deep learning, that can be used on HiRISE images across Mars. This algorithm will provide a useful ‘first-pass’ analysis. It will take a HiRISE image of interest and use semantic segmentation to provide an enriched classification product which will aid researchers in quickly and confidently understanding the nature of the terrain, without having to carry out time consuming manual analysis. The project will seek to test the effectiveness of Deep Learning algorithms using already-available data.

Behavioural competencies

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

Education

You must be a university student, preferably studying 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.

• Strong command of Python, and experience with TensorFlow, PyTorch, or similar.
• Experience of semantic segmentation and machine vision is desirable.
• An understanding of Mars geology and geomorphology is desirable but not required.
• Additionally, candidates should have a familiarity with Mars image datasets, raster data handling, projection systems, and image georeferencing.

Important Information and Disclaimer

During the recruitment process, the Agency may request applicants to undergo selection tests.

The information published on ESA’s careers website regarding internship conditions is correct at the time of publication. It is not intended to be exhaustive and may not address all questions you would have.

Nationality

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