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Key takeaways To bring computing power to the edge and to make the cloud sustainable, various paradigms for energy-efficient computing are emerging. These paradigms, for example neuromorphic, wave
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Division, Systems Department, Directorate of Technology, Engineering and Quality. The Future Engineering Division, in support of the programme directorates, is responsible for developing and providing
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on carbon turnover models and the fusion of data science forecasting methods with process-based models (hybrid modelling) to map soil and biomass carbon fluxes across Europe at high resolution. Your
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Vacancies Academic staff Support staff UT Student Jobs UT as employer UT as employer Employment conditions Career and development Pre and onboarding HR Excellence in Research Tenure Track PhD EngD Working as a UT student Stories from UT employees Job alert About UT Campus Research...
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isotachophoresis (dzITP) device for highly efficient preconcentration, separation, and filtration of biomolecules. The next phase of this work involves coupling this microfluidic technology directly to MS—leveraging
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Mathematics, Computer Science and Artificial Intelligence. The environment fosters truly interdisciplinary research with a great track record of national and international collaboration transferring knowledge
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Mathematics, Computer Science and Artificial Intelligence. The environment fosters truly interdisciplinary research with a great track record of national and international collaboration transferring knowledge
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research centre for life sciences. The themes we deal with are relevant to everyone around the world and Wageningen, therefore, has a large international community and a lot to offer to international
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21 Aug 2025 Job Information Organisation/Company University of Groningen Research Field Juridical sciences » Environmental law Juridical sciences » Public law Researcher Profile Recognised
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: 12 September 2025 Apply now Join the faculty of Geosciences as a postdoctoral researcher! You will work on carbon turnover models and the fusion of data science forecasting methods with process-based