Intern in The Data Science and Archive Division

hace 2 semanas

Madrid, España European Space Agency A tiempo completo

**EUROPEAN SPACE AGENCY**

Intern in the Data Science and Archive Division

**Job Requisition ID**:18111**Closing Date**:30 November 2023 23:59 CET/CEST**Publication**:External Only**Vacancy Type**:Intern**Date Posted**:1 November 2023- Internship Opportunity in the Directorate of Science.-
**Location**
Madrid

**Our team and mission**

**This position is based at the European Space Astronomy Centre (ESAC) - Near Madrid, Spain**
Under the direct authority of the Director of Science, the Science and Operations Department is responsible for ensuring that maximum scientific return, within applicable constraints, is maintained as an objective for the Directorate’s missions through their lifetime, by providing scientific oversight of the Directorate’s missions throughout their lifecycle, managing and being responsible for the operation of the Directorate’s missions once successfully commissioned, and curating the scientific data in their legacy phase, while establishing and maintaining the necessary science interfaces to the community. These responsibilities are discharged in full coordination with the Directorate’s Departments and Offices and, as appropriate, with the Directorate of Operations.
In implementing its duties, the Science and Operations Department is supported by the:

- Science Division
- Mission Operations Division
- Science Operations Development Division
- Data Science and Archive Division
- Mission Support Office

**Field(s) of activity for the internship**
You can choose between the following topics:

**1) Topic 1: Interactive data tutorials for Mars Express**
After orbiting Mars for more than 20 years, the Mars Express (MEX) mission has built an extensive collection of data from its eight instruments. All of these data are made freely available to the public in ESA's Planetary Science Archive (PSA). However, due to the diversity and complexity of the data, it is not always easy to start analysing them. This project aims to produce a set of Jupyter Notebooks which guide new users through the process of searching for data, processing it and producing meaningful results. Combined with the ESA DataLabs project, which will allow users to run, modify and create their own notebooks running alongside the data, this will give the planetary science community a powerful tool to quickly start producing new science from MEX.
For more information on this topic, please visit: Interactive data tutorials for Mars Express

**2) Topic 2: Shaping the future of Science Operations through Natural Language Processing**
One of the areas critical to the success of ESA's science missions is science operations. How can we simplify the process for upcoming missions to effectively express their science operation requirements while capitalizing on commonalities across multiple missions?
The primary objective of this traineeship is to harness the latest advancements in natural language processing and implement these techniques within the realm of space science missions at ESA. In this project, you will employ open-source, large language models like LLAMA2 and enhance their capabilities through fine-tuning using information sourced from ESA's science documents. The ultimate goal is to construct an AI agent capable of extracting knowledge, summarizing information, and identifying similarities within a substantial body of text pertaining to scientific requirements. This agent will be employed by new missions to tap into the pre-existing requirements upon which science operation centers have been established, reducing the need to create entirely new requirements solely for the specific operations of new missions. This approach will facilitate a more streamlined and faster establishment of science operation requirements.
Fore more information on this topic, please visit: Shaping the future of Science Operations through Natural Language Processing

**3) Topic 3: Machine Learning with ESASky**
The ESASky (sky.esa.int) portal hosts and serves a large number of data sets in HiPS (Hierarchical Progressive Surveys) format from missions such as XMM-Newton, HST, JWST and Spitzer, among many others. These HiPS maps have been produced at different Healpix resolutions tesselating the sky in tens of thousands of small png or jpeg images that are used by ESASky to display the sky at different zoom levels. These HiPS files are a valuable resource for training machine learning models and are publicly available at the ESASky servers. However, accessing and navigating these repositories is not straightforward and users would benefit from a dedicated mechanism to serve these images. The ESA Datalabs Science Exploitation platform already provides access to a wide variety of data sets, but not the HiPS files from ESASky. During this internship, you will develop with us the mechanism to give users the possibility to access and use these images in ESA Datalabs. This will provide new valuable datasets for running ML experiments in ESA Datalab

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