26 computer-science-cryptography-"Multiple" positions at Linköping University in Sweden
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30 Aug 2025 Job Information Organisation/Company Linköping University Research Field Computer science » Digital systems Technology » Information technology Technology » Interface technology
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facilitate data sharing among actors involved in a new circular flow of flat glass. Within the project, two PhD students, one at the Department of Computer and Information Science (with computer science
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application! Work assignments As a postdoctoral fellow, your main task will be to conduct cutting edge computational social science research. The research will be carried out within the context of the Swedish
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application! Work assignments As a postdoctoral fellow, your main task will be to conduct cutting edge computational social science research. The research will be carried out within the context of the Swedish
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application! We are seeking up to two advanced-level students in computer science, computer engineering or closely related area as research project assistants (programming, system administration). The position
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and advanced levels, primarily in our engineering program in Construction Engineering and our master's program in Digitalized Construction. Course orientations where you may be involved include
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educational technology on learning in public educational spaces. You are expected to produce research outputs relevant to the fields of visual learning and communication, and computer science. These outputs
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application! We are looking for a PhD student in Medical Science. Your work assignments As a PhD student, you will participate in the project: Predictive markers for chemotherapy-induced toxicity in childhood
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undergraduate and advanced levels, primarily in our engineering program in Construction Engineering and our master's program in Digitalized Construction. Course orientations where you may be involved include
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description