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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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of intermediate complexity to investigate the climate of the Paleoproterozoic interact with colleagues in the GOE-DEEP science team working on biogeochemical modelling to design and tests different scenarios
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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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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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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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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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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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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