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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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collaborative mindset and bring the following qualifications: a PhD degree in mathematics, with a focus on algebraic geometry; experience with at least some of the following: Fourier—Mukai transforms, Hochschild
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PhD Position in Algebraic Geometry Faculty: Faculty of Science Department: Department of Mathematics Hours per week: 36 to 40 Application deadline: 7 July 2025 Apply now Join our dynamic
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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