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mathematical modeling of infectious diseases applied to grided spatiotemporal data inputs. The project involves reviewing existing literature and especially synthesize model structures and parameters for the eco
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optimization of imaging parameters. Overall, this project aims to explore, develop and integrate advanced imaging modalities towards their use for biological samples. Work tasks The main task of the position is
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Postdoctoral position in fabrication of hollow-core optical fibers for next-generation communication
postdoctoral researcher, will mainly carry out: optimization of both fiber preform preparation and fiber drawing processes so as to reliably fabricate HCFs with desired geometrical parameters for an extended
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HCFs with desired geometrical parameters for an extended fiber length (beyond 10 km), and characterization of the fabricated HCFs (e.g. optical transmission properties). Apart from the main tasks above
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intended support design decisions related to manufacturing and process parameter selections; known to influence the material microstructure. Duties: The post doc will develop and apply computational methods
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; significant practical experience in 3D image analysis or computer vision; knowledge and experience in scientific programming (python (preferred), Matlab or other relevant language) with application to image
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of mathematical areas. The position will be placed at the Department of Computer Vision and Machine Learning (CVML) at the Mathematics Centre (https://maths.lu.se/). Mathematics Centre is a department
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potential, including bulge, disc and halo, to be constructed. Likewise LISA will detect GW parameters which could be used to constrain and improve Gaia or GaiaNIR solutions. This holistic approach
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(e.g. in automotive applications) using, for example, vibration and acoustic signals. Research questions include method development for fault detection and isolation, damage estimation systems, including
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measurements, vegetation inventory and biodiversity assessments for a set of diverse continuous cover forestry systems in boreal Sweden. The project will develop bottom-up estimates of the forest ecosystem