Our team and mission

Within the Earth Observations Projects Department, the Copernicus Sentinel Expansion missions are currently under development. One of those missions is the CHIME (Copernicus Hyperspectral Imaging Mission for the Environment). CHIME plans to carry a unique visible to shortwave infrared spectrometer to provide routine hyperspectral observations to support new and enhanced services for sustainable agricultural and biodiversity management, as well as soil property characterisation.

You will join the Copernicus Hyperspectral Imaging Mission for the Environment project and work with the system and satellite engineering teams on on-board artificial-intelligence processing and the evaluation of end-to-end mission performance.

Further details on the CHIME mission can be found at:

• ESA - Copernicus Sentinel Expansion missions;
• ESA - Going hyperspectral for CHIME;
• CHIME: an overview of its mission, system and planning status; SPIE Proceedings Volume 12729; 1272909 (2023) The copernicus hyperspectral imaging mission for the environment (CHIME): an overview of its mission, system and planning status;
• CHIME's end to end simulator (CHEES): simulating, testing and generating hyperspectral products;
• Adrien IGARSS paper;
• Examples for typical CHIME Level 2 products: Prototyping Vegetation Traits Models in the Context of the Hyperspectral Chime Mission Preparation | IEEE Conference Publication | IEEE Xplore, Retrieval of carbon content and biomass from hyperspectral imagery over cultivated areas - ScienceDirect.

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

Field(s) of activity for the internship

Topic of the internship: Event-Aware On-Board Processing for CHIME: From Detection to Operations

Join the Copernicus Hyperspectral Imaging Mission for the Environment and help turn two public research prototypes into an integrated mission-level on-board processing concept. Previous work produced public results on marine-anomaly screening and night-time fire detection. You will integrate these methods into a configurable decision layer that identifies scientifically valuable pixels and controls how data are preserved, compressed and prioritised for downlink.

Core activities

• define configurable processing modes for land, ocean and night-time observations;
• integrate the existing compact detection methods into the mission end-to-end simulator;
• connect detection masks with selective compression and data-prioritisation policies;
• quantify detection performance, false alarms, scientific-data preservation and potential downlink savings;
• assess robustness across different geographic regions, seasons, illumination conditions and representative instrument effects;
• define operating limits, verification criteria, traceability information and fallback behaviour;
• deliver reproducible software, trained models, documented datasets and a technical report suitable for public presentation or publication.

Depending on the progress

• develop a staged validation concept using reference and candidate processing configurations, including possible use of the two-satellite constellation.

Learning objectives

By the end of the internship, you will be able to:

• translate machine-learning prototypes into a mission-level processing and operations concept;
• evaluate detection performance together with data-volume, product-quality and operational constraints;
• design reproducible end-to-end simulation and validation campaigns;
• understand how on-board processing interacts with satellite operations, data compression and ground processing;
• define operational limits, verification evidence and safe fallback behaviour for configurable processing methods;
• communicate results through clear technical documentation, well-structured software and quantitative evidence.

You will work with mission engineers, data-processing specialists and Earth-observation scientists. You will have access to mission simulation tools, curated datasets and the results of previous internships.

The principal outcome will be a reproducible prototype and validation package demonstrating how event-aware processing could increase mission value while maintaining scientific traceability and controlled operational complexity.

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:

• background in machine learning, computer vision, signal processing or remote sensing;
• practical experience with Python and a deep-learning framework;
• familiarity with hyperspectral data, transfer learning, model compression or uncertainty estimation is an asset.