542 computer-programmer-"U.S"-"Embry-Riddle-Aeronautical-University"-"U.S" positions in Netherlands
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funded PhD position and you will be offered a course program tailored to your needs and the research team. The gross salary for the first year is € 3059,-. per month rising to € 3881,- in the fourth year
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Location EAC, Porz-Wahn, Germany Our team and mission The CAVES and PANGAEA team specialises in training programmes that equip astronauts and mission developers with scientific, expeditionary and
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analysis and dose verification (depending on your experience). What you will do As a postdoctoral researcher, you will: Independently carry out experimental and computational research within
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programme Molecular and Biophysical Life Sciences; willingness to obtain the University Teaching Qualification if you do not yet have it; a track record of international publications in leading journals; high
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Infomatter group at AMOLF. The SMIP project aims at revolutionising computing by developing adaptive, smart materials that combine memory and learning directly within their structure, uniting theory and
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This post is part of ESA’s Junior Professional Programme. We are looking for enthusiastic candidates with up to 2–3 years of relevant professional experience after their Master's degree to join ESA
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Regions, Smart Industry and Sustainable Resources. Our faculty is home to about 2,900 Bachelor's and Master's students, 550 employees and 150 PhD candidates. Our educational and research programmes
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progress in the first year to indicate that a successful completion of the PhD thesis within the contract period is to be expected. A PhD training programme is part of the agreement and the successful
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Exploration Programmes is a programme directorate within the ESA matrix structure. Through a partnership between humans and robots, D/HRE is dedicated to exploring destinations in space where humans are already
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/video segmentation, object tracking, reinforcement learning. Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation