50 computer-security "https:" "https:" "https:" "https:" "https:" "https:" uni jobs in Netherlands
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) of the Earth Observation Programme Directorate (D/EOP). EOP-FM’s role is to prepare the Earth observation (EO) missions and technologies of the future, encompassing a wide range of scientific missions (Earth
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the roll-out of its services, building the Galileo 2nd Generation and defining the future of the European GNSS. You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity
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space ambitions, delivering tangible, measurable and long-term impact for future missions. You are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity/research for the traineeship
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7 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 32.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Restoring nature and reversing biodiversity loss are urgent
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38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Due
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Neuromorphic photonics is
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description (Re-opened vacancy) Wageningen University & Research (WUR) invites
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Eindhoven University of Technology, Department of Mathematics and Computer Science Position ID: TUe-MB [#27895] Position Title: Position Type: Tenured/Tenure-track faculty Position Location
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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