13 high-performance-computing Postdoctoral research jobs at Nature Careers in Denmark
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
to update our scientific conceptual models. The data foundation will come from the long-term lake monitoring initiative LTER-DK, which includes several Danish lakes equipped with real-time and high-frequency
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detection methods Background in public outreach or science communication Familiarity with high-performance computing environments Proficiency in Danish and/or German Who we are The Department of Ecoscience is
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that facilitate the understanding of spatial profiling in clinical samples. You will work with datasets from our Xenium High-plex Single-cell Spatial Profiling (HSSP) from 10x Genomics and GeoMx Digital Spatial
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models of complex physical systems starting from data, ranging from robotic systems to traffic and turbulent flows. We are implementing these methods in high-performance open-source libraries to make them
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and around 825 students are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD students have high academic ambitions and deliver high-quality results for both
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research. Experience with high-performance computing (HPC) environments. A strong publication record relative to career stage. Ability to organize tasks and work in an interdisciplinary team. Further
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to transform the entire structure and functioning of Arctic coastal marine ecosystems. CIFAR is a research center that aims to unravel how the complex interplay between ice melt, runoff and ice formation across
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Master of Science in Engineering and around 825 students are enrolled in our study programs. Furthermore, we also offer an ambitious PhD program. Our PhD students have high academic ambitions and deliver
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ecological communities reassemble under extreme and unprecedented disturbance regimes. A core goal is to identify the key traits that govern colonization, stress tolerance, and ecosystem functioning in post
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cellular processes efficiently. This project aims at understanding the formation and functioning of aggregate-forming Archaea-Bacteria partnerships. The project involves working with syntrophic deep-sea