212 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" uni jobs in Netherlands
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semiconductor manufacturing company ASMPT and the Artificial Cognitive Systems lab at the Department of Machine Learning and Neural Computing of the Donders Institute, Radboud University, headed by Prof. Marcel
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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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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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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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are required to have proven work experience and good knowledge of the following: computer-aided design tools, preferably 3DExperience; simulation tools, preferably Nastran for Finite Element analysis and MATLAB
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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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the ESCALATION study external link on early-onset breast cancer; apply advanced statistical, and machine-learning methods to identify and validate environmental determinants of breast cancer risk; integrate