91 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" uni jobs at European Space Agency in Netherlands
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advanced techniques (such as digital beamforming or machine learning/Al techniques) supporting instrument operation and operative modes; onboard data compression; onboard data encryption; ASICs and devices
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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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on the optimisation of the data architecture to enable the efficient use of artificial intelligence and machine learning. Duties These positions combine EO domain knowledge (EO instruments, EO data, EO algorithms
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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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Knowledge of EO space mission operations and operations planning Knowledge of specific characteristics of military and dual use space systems Behavioural competencies Education A master's degree in
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characteristics 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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or hands-on hardware (including integration) experience Artificial Intelligence and Machine learning techniques for AOCS applications and engineering The motivation for supporting engineering laboratory
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for the following: Standard metrology tools, e.g. laser trackers, theodolites and CMM machines Laser radar Photogrammetry Thermography Alignment methods (contact and contactless) Calibration of light sources for sun
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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