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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 Passionate about sustainable steel production? Join us in
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5 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Programming Researcher Profile Recognised Researcher (R2) Country
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Postdoctoral Researcher in Innovation&Investment Policy for Hydrogen Clusters in the EU(0.8-1.0 fte)
research programme GroenvermogenNL, which supports the large-scale development and implementation of green hydrogen in the Netherlands and across Europe. The programme addresses the system-wide conditions
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); units such as onboard computers, mass memories, remote terminals and instrument control units*; digital and analogue signal processing electronics for payload/platform functions; front-end acquisition and
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. Demonstrated experience of using qualitative research methods and a willingness to develop expertise in further methodological work. Awareness of and/or expertise in the United Nations’ peace operations Country
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these with quantitative and qualitative research methods to promote a regenerative food system in the Netherlands? If you have a PhD degree and a skill set that puts you in a good position to carry out
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(MPRR) Group at Technical University Delft has joined the National Growth Funding “Groeien met Groen Staal ” (GGS)” programme which aims at making the Dutch steel sector circular, CO2-neutral, and high
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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
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to have the following skills and experience. We welcome applications from individuals with experience in: Deep learning Medical image computing (preferably x-ray imaging) Computationally efficient deep
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learning Medical image computing (preferably x-ray imaging) Computationally efficient deep learning Deep learning model generalisation techniques Translating deep learning models into clinical settings