28 parallel-computing-numerical-methods positions at Utrecht University in Netherlands
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interdisciplinary environment within EMBRACER and international partners and apply advanced methods like Lagrangian tracking and reanalyses to reveal new insights into atmosphere-ice-ocean feedbacks in the Arctic
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your motivation letter. This project is part of the 10-year EMBRACER research programme funded by the Dutch Research Council (NWO). At EMBRACER, we work at the very frontiers of knowledge on climate
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knowledge graph models of such transformations that are self-describing when applied to existing map repositories, and can be scaled up to large data repositories using state-of-the-art AI methods. In
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between these important climate subsystems, using models of different complexity and a combination of mathematical, numerical and data analysis methods. You can find the other vacancy at PhD Position in
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. Drawing on insights from hydrology and quantitative risk management, the project will also develop improved methods to identify energy droughts and detect "worst-case" weather years for energy security
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integration of clinical product development into academic research. Research: Your role involves leading a small research team dedicated to explore novel methods for the production and advancement of Advanced
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gradient that determines atmospheric water transport to extra-tropical regions. To this end, you will generate proxy data to assess temperature, hydrology and carbon cycle information. Methods might include
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related field; experience with numerical modelling, preferably hydrological modelling; affinity with delta systems, adaptation and policy analysis; motivation to work in interdisciplinary scientific
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programming skills (Fortran, Python or similar); affinity with spatiotemporal data analyses and numerical modelling; strong reporting and presentation skills; a good level of written and spoken English. Our
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Application deadline: 15 May 2025 Apply now Are you fascinated by the complex ways our social environments shape health? Do you want to develop cutting-edge methods to measure social networks at a population