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Are you interested in architected materials, their mechanics and physics and you want to contribute to the development of the area that would combine fracture mechanics with statistical and
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biologging multi-sensor acoustic tags on bats and toothed whales in the wild and combine these data with array recordings, noise playbacks and phantom target generators on trained animals in the lab
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Are you interested in architected materials, their mechanics and physics and you want to contribute to the development of the area that would combine fracture mechanics with statistical and
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will be at the absolute forefront of combining GNSS and modeling the different contributions that courses solid Earth deformation where the main contributors are elastic deformation, glacial isostatic
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for materials discovery. Project description Machine learning opens up new opportunities to accelerate the discovery of next-generation energy materials by combining predictive and generative approaches. In
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can use and combine various cutting-edge data modes such as single-cell ATAC-seq, single-cell RNA-seq, spatial gene expression, and whole-genome sequencing. The candidate will get the opportunity
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an exciting, emerging topic. The position will focus on integration and device fabrication based on combinations of 2D materials and freestanding complex oxides, aiming at discovering, engineering and unlocking
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of acetylation and other chromatin modifications in gene regulation. The project will combine CRISPR-based genome editing with NGS-based approaches (i.e. RNA-seq, ChIP-seq, ATAC-seq) and cell biological approaches
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protein design tools to generate and prioritize inhibitor candidates with high predicted binding and selectivity. These designs will then be experimentally validated through a combination of affinity
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organization and management as a practical, situated, contested and changing matter. Our approach is interdisciplinary and combines social-scientific traditions such as organization theory, sociology