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to the research project at hand, having very good skills in discourse analysis, as well as an orientation in digital politics, are key requirements. Since the research will be conducted in an international and
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on the following criteria: Knowledge in electric power engineering, power electronics, and power system analysis Experience in modelling, simulation, and experimental work Proficiency in Swedish and English, both
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emphasis on Image Analysis and/or Geomechanics Fluency in spoken and written English Willingness to learn Swedish, as necessary for providing teaching support at undergraduate level Genuine interest in
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risk analysis. -Testing for security and security countermeasures analysis and implementation -Access and usage control for secure data sharing in industrial eco-systems. -Virtualization at the cloud
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experience in statistical analysis are also advantageous. The applicant should be able to work both independently and as part of a team. Proficiency in both spoken and written English is required. The ability
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function of retained trees for lichens will be evaluated. This will be done by field studies, analyses of already collected data, and a meta-analysis of published studies. By working at Kopparfors, the Ph D
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on: Technical Expertise: Documented skills in Python, Matlab, R, and a strong working knowledge of UNIX environments. Proven familiarity with biological omics data analysis techniques is essential, along with any
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mechanical analysis, nanonindentation and atomic force mircroscopy will be used to characterize the mechanical properties of (doped) conjugated polymers. You will work closely with fellow PhD students and
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work. A model is to be developed to estimate the material mass breakdown for various cell designs and cell formats. The model will be validated from teardown analysis of commercial lithium-ion battery
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the training process to several network threats, such as DDoS attacks, traffic hijacking, and traffic analysis. While these risks are well-studied in existing literature, their impacts on distributed AI training