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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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information, agronomic data, and artificial intelligence methods. Assess drought stress and identify physiological traits associated with drought tolerance using advanced imaging technologies. Develop machine
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within the Department of Electrical and Information Technology works broadly with research within Cryptography, Computer Security, Wireless and Fixed Networks. The security group has around 20 members
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KTH Royal Institute of Technology, School of Engineering Sciences Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics at KTH and Science for Life Laboratory is a
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tasks include: AI support in the process chain: developing and applying image analysis, data process analysis, and sensor technology for textile production; using machine learning for process optimisation
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networks (NTN), in particular low Earth orbit (LEO) satellites and high altitude platform stattions (HAPS) are highly advantageous Knowledge in digital twinning is highly advantageous Knowledge in machine
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candidates whose expertise falls within one or more of the following areas: computational and mathematical modeling, statistical modeling, machine learning, network science, bioinformatics, applied mathematics
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of computer scientists. Extent: 100% employment, distributed as 80% research and 20% departmental duties (typically teaching at the BSc or MSc level). The position is meritorious for future roles in
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well as a highly collaborative research environment. For more information about Nathaniel Street’s research group, see: https://www.umu.se/en/staff/nathaniel-street/ Project description Establishing robust
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systems to communication solutions and biomedical engineering. The Electrical Machines and Power Electronics unit conducts research and education in electrical machines, power electronics, drive systems