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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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PhD or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological science with demonstrated experience in computational and statistical
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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: completed scientific higher education degree (PhD) in the field(s) of Earth system science, physics, climate physics, geosciences, mathematics, computer science or a comparable field demonstrated experience
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Skip to main navigation Skip to content Skip to footer Skip to search Uni A-Z Contact Search Search (via Ecosia) Search Search Login Login Login Form Login Language Choose language German English Mainmenu Information for Prospective Students Current Students Staff Teaching...
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The Leibniz-Institut für Wissensmedien (IWM) conducts research into the acquisition and sharing of knowledge and cooperation with digital media. The institute is a member of the Leibniz Association (Leibniz Gemeinschaft) and committed to international standards of excellent science. The IWM...
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The Peace Research Institute Frankfurt (PRIF) analyses the causes of international and intrastate conflicts. As one of the leading peace research institutes in Europe, we combine interdisciplinary basic research with science transfer to the public, the media and policymakers. Since 2009, the...
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, oceanography, physics or mathematics, with a strong interest in the application of statistical and data analysis methods #excellent knowledge in UNIX/Linux and Unix-Scripting #very good knowledge in programming
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degree and PhD degree in meteorology, oceanography, physics or mathematics, with a strong interest in the application of statistical and data analysis methods excellent knowledge in UNIX/Linux and Unix
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, Computational Biology, Applied Mathematics or related field Interest in interdisciplinary research Applied experience with machine/deep learning methods Ability to handle multiple projects in a dynamic