571 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions in Netherlands
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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
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You will join the “Professional Learning & Technology” (PLT) section of the Faculty of Behavioural, Management & Social Sciences. The PLT section specializes in research on professional learning in and
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of moving to a non-English-speaking country posed a challenge. I questioned my supervisor multiple times about the necessity of learning Dutch. Fortunately, she assured me that the English level is very high
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computational lens. This calls for strong expertise in computational methods, machine learning, and data modelling combined with solid knowledge of music. We particularly aim to cover a broad range of musical