83 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" uni jobs at European Space Agency
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Lab). You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity/research for the traineeship As a member of the GNC, AOCS and Pointing Division in the Electrical Department
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encouraged to visit the ESA website: http://www.esa.int Field(s) of activity/research for the traineeship You will contribute to a range of activities of the Technology Coordination and Planning Office, in
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Structures Inflatable and Deployable Structures Launchers / Re-Entry (Hot Structures) / Planetary Landing Vehicles Crew Habitation / EVA Suits You are encouraged to visit the ESA website: http://www.esa.int
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features of military and dual-use space systems Behavioural competencies Education A master’s degree in engineering or a scientific discipline is required for this post. Additional requirements You should
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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
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intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
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value. Knowledge of computer systems and information/planning/coordination tools (such as esa-p, Microsoft Excel and Microsoft Project) is essential. Familiarity with modern dashboard and risk management
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lessons learned and best practices, to enable learning, ensure consistency, and improve quality across teams and projects) Behavioural competencies Education A master's degree in a relevant domain is
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management (Ability to capture, organise, and share knowledge, including lessons learned and best practices, to enable learning, ensure consistency, and improve quality across teams and projects) Behavioural
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; streamlining, improving and providing regular training on ground segment processes related to the Navigation Quality Management System; ensuring that the lessons learned for the Galileo ground segment