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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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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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position in active flow control for wingsail performance and operation. The goal is to develop innovative flow control techniques to optimize aerodynamic performance and reduce vibrations in unsteady
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authentication and usable security (e.g., biometrics, CAPTCHA); Privacy (e.g., information inference); Intersection between regulatory and technical aspects (EU NIS-2 Directive), e.g., compliance optimization and
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, design and characterization of quantum processors Development and optimization of nano-fabrication processes for large-scale devices Development of optimal control techniches to achieve fast and high
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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for tens of kilometers length. In this project, you as a postdoctoral researcher will mainly carry out: optimization of both fiber preform preparation and fiber drawing processes so as to reliably fabricate
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immunohistochemistry. Flow cytometry and cell sorting. High-throughput screening approaches. Development or optimization of molecular methods. Postdoctoral scholarships may be established for foreign researchers who
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focuses on the development of new materials and processes for electronics packaging and bioelectronics applications. The research of the electronics packaging group in the Electronics Materials and Systems
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research questions. This postdoctoral scholarship offers the opportunity to be a part of this AI revolution by developing novel neural network architectures specifically optimized for plant genomic data. Our