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should be tested against data from the present and past, going back to 6000 years before today, and they should be enhanced to predict indicators of Nature’s Contributions to People (NCPs). Together
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the design of an AI test environment for open-source Large Language Models (LLMs). Research on the influence of low-frequency expert data on LLM-based systems and development of robust strategies
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predators and parasites. We investigate these links by studying how performance in cognitive tests and effectiveness in navigating among resources co-varies with behavioral strategies in these contexts and
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physicochemical characterization (SEM, SAXS, DLS, DSC, TGA, DMA, rheology, adhesion testing, FTIR, Raman, XPS) Quantify adhesion, cohesion, rheological, thermal, mechanical properties Establish structure–property
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with tailored morphology Develop processing routes Perform advanced structural and physicochemical characterization (SEM, SAXS, DLS, DSC, TGA, DMA, rheology, adhesion testing, FTIR, Raman, XPS) Quantify
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monitoring relevant parameters, as well as undertaking experiments with e-waste decay; study foraminifer tests with microscopy (e.g. SEM, LM); collaborate with partners in the USA and within the LRA-AMS
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of these devices for different application scenarios and iv) testing on both laboratory and real environment conditions. Notably, this young scientist will be involved in the International Joint Research Center
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the framework of the LPJ-GUESS Dynamic Global Vegetation Model. Geologists in the team will advance the state-of-the-art in reconstructing Oligocene to Miocene C3 /C4 transitions, which will be used to test the
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the project framework. Designing and conducting reproducible experimental pipelines (including baselines, ablation studies and robustness and out-of-distribution tests), including the use of game-based or game
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an equivalence test in Germany, which is subject to a fee. This must be presented in the event of a later hiring: https://zab.kmk.org/en/statement-comparability