19 machine-learning "https:" "https:" "https:" uni jobs at European Space Agency in Netherlands
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are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Artificial Intelligence / Machine Learning for ECSS space standards requirements
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variations). As part of the laboratory activities, new challenges are identified: in particular, the use of Machine Learning techniques to predict atomic clock anomalies based on the processing of its
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wind speed and sea ice extent. The baseline algorithms designed to estimate such variables from the observed data at Level 1 (L1) are based on Machine Learning (with the exception of that for Freeze/Thaw
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) Topic 4: On-board Digital Signal Processing and Machine Learning/Artificial Intelligence The objective of this topic is to model and implement functions commonly used in on-board processing, assessing
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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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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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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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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