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into your PhD dissertation, supported by experienced GEM researchers; you design and apply innovative computational methods such as machine learning, to extract meaningful insights from GEM and complementary
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-resolution, open-access climate projection ensembles with statistical and machine learning-based resampling techniques (e.g., k-nearest neighbours) to simulate weather-dependent energy supply and demand
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spoken and written. Additional experience that would be considered a plus: some experience in programming and the use of computer algebra in algebraic geometry; experience with noncommutative algebraic
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algebraic geometry, or representation theory; familiarity with programming and the use of computer algebra. Our offer A position for 18 months, with an extension to a total of four years upon successful
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; experience with AI and machine learning methods, especially in the areas of natural language processing or graph neural networks; the ability to work independently and collaboratively in an interdisciplinary
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observations. Your major challenge is in model development, and there is room for you to develop machine learning applications in the field of firn modelling. If successful, your work will lay the foundation
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with strong expertise in one of these categories: solid-state NMR; Quadrupolar solid-state NMR; Automated NMR analysis & machine learning; Lipid biochemistry (and chromatography knowledge in general