Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
aperture synthesis) and testing (calibration and performance verification). You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity/research for the traineeship As a trainee, you
-
. Specifically, your research will provide critical insight for NGGM performance assessment and predictions. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s) of activity/research
-
Location ESA Headquarters, Paris, France Our team and mission As part of the ESA Directorate of Space Transportation, the Future Space Transportation Preparation team (under the FLPP programme
-
programme management across the Agency consistent, transparent, and future-ready. As the custodians of the Programme and Project Management Requirements, we ensure that these standards evolve with
-
This is a post for a limited duration of three years. Location ESAC, Villanueva de la Cañada, Spain Description ESA maintains a world-leading Science Programme with missions in heliophysics
-
) of the Earth Observation Programme Directorate (D/EOP). EOP-FM’s role is to prepare the Earth observation (EO) missions and technologies of the future, encompassing a wide range of scientific missions (Earth
-
space ambitions, delivering tangible, measurable and long-term impact for future missions. You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity/research for the traineeship
-
the roll-out of its services, building the Galileo 2nd Generation and defining the future of the European GNSS. You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity
-
transformative innovation in the sector. Our vision is to become an EO innovation hub connecting EO with a growing ecosystem of disruptive and transformative innovation like AI, ML, quantum computing, edge
-
are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship You can choose between the following topics: 1) Topic 1: Machine Learning for recognition of planetary materials