131 postdoc-in-postdoc-in-automation-and-control-"Multiple" Postdoctoral positions in Sweden
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above, the postdoc can be involved in the following research areas if interest arises: characterization of fabricated HCFs (both structural and optical transmission properties), and experimental
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postdoc with a strong background in cell or tissue biology. The ideal candidate should have practical experience working with mouse models and analyzing sequencing data. Experience with organoid cultures
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topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks above, the postdoc can be involved in
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that will be affiliated with one of six possible multidisciplinary projects. The ideal postdocs will have expertise in some of the following areas: computational modeling, computational biology, computational
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at basic, advanced and/or postgraduate levels may be included in the duties. In addition to the mentor’s group, the postdoc will collaborate with national and international experts in the field
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experiments, samples from world-unique CO2 experiments, cutting-edge NMR spectroscopy and isotopomer analysis (doi 10.1111/nph.20358). Two postdocs will work together to conduct plant ecophysiology experiments
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experiments (both in the lab and in the field), coordinating cross-cultural studies involving multiple international research groups, data analysis, computational modeling, and writing scientific articles
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LiDAR and UAV photogrammetry) with physiological and spectral indicators of forest health. The research will be conducted at multiple spatial scales, from single trees to landscapes, and integrated
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LiDAR and UAV photogrammetry) with physiological and spectral indicators of forest health. The research will be conducted at multiple spatial scales, from single trees to landscapes, and integrated
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mathematics, ecology, history, climatic and medical sciences in collaboration across multiple institutes. An integral part of the project is to develop process-based eco-epidemiological models considering