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Field
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of turning cutting-edge machine learning into operational environmental monitoring from space—and you enjoy balancing scientific depth with practical constraints—this internship is for you. Behavioural
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intelligence and machine learning techniques; Pointing engineering, including stabilisation techniques and control strategies; Failure Detection Isolation and Recovery (FDIR) for AOCS and GNC systems; State
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intelligence and machine learning techniques; Pointing engineering, including stabilisation techniques and control strategies; Failure Detection Isolation and Recovery (FDIR) for AOCS and GNC systems; State
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: communicative, empathy, influence without authority, analytical thinking, and a collaborative mindset. Bonus skills: experience with change management, machine learning, basic programming, or research software
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interested are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Analysis of CRISTALair Functional Flight Campaign Data Due to climate
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for applying to this role will be explored. Knowledge and background in one or more of the following domains is an asset: artificial intelligence and data science: understanding of machine learning and deep
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applications engineering; database engineering, data science; AI and machine learning; GIS. Natural curiosity, creativity, and a can-do attitude are very important. Moreover, although you will be part of a wider
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from, collaborate with, support, or improve humans; Deep Learning for Perception: Use of deep learning algorithms for computer vision, image and audio processing, and models of perception. The focus is
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/scientific fields; Programming experience in languages such as Python or C++; Knowledge of Nvidia Omniverse; Familiarity with AI or machine learning techniques applied to simulation, control, or optimisation
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methodologies, such as additive manufacturing, for projects within the centre and for space exploration; Developing new ideas around medical technologies, for example, using machine learning techniques to support