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, and portfolio optimization. Despite the growing relevance of ESG in insurance, there is no widely adopted, data-driven framework that integrates ESG metrics into customer portfolio decision-making
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fundamental methods research to guide in these analytical challenges. In this PhD-project, you will propose and evaluate new AI methodology to ensure that organoid data can be used to optimally predict relevant
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this information will not be considered. Position #1 - experimental In this PhD study, you will evaluate and help optimize a highly diverse intercropping system. You will assess which combination of service crops
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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on melting performance. Apply a multiscale characterization strategy to link microscopic phenomena to macroscopic process outcomes. This fundamental research will contribute to optimizing the HyDR process and
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performance and durability at appropriate temperatures. Characterize materials using different techniques, and link properties to performance. Optimize reaction conditions and explore scale-up potential
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will evaluate and help optimize a highly diverse intercropping system. You will assess which combination of service crops and their management by robots can reduce the need for agricultural inputs
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; Modeling (using Gromacs and LAMMPS software packages and MARTINI-like upscaling) the filler-matrix interfaces to optimize thermal transport, e.g., by functionalization of the nanofiller surface; Simulate and
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examine hypotheses in a truly physiological context. In this project, you will contribute to both engineering and neurobiology. First, you will help build a light-sheet microscope optimized for voltage
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. Process simulation software is being developed for virtual optimization of tool design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies