36 computer-science-programming-languages positions at Linköping University in Sweden
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. The task is to understand phenomena like “model collapse” and implementing strategies to avert it, using tools from the Systems and Control field. Wallenberg AI, Autonomous Systems and Software Program (WASP
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independently and in groups. Excellent written and verbal communication skills in English are essential. Your workplace You will work at the Department of Biomedical Engineering (IMT) at Linköping University
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qualifications You have graduated at Master’s level in machine learning, statistics, computer science, or a related area that is considered relevant for the research topic of the project, or have completed courses
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of high international standing. At undergraduate level the department is responsible for most of the medical program, the speech and language pathology program and the biomedical program. For more
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Technologies for Transformative Change' and 'Exploring the Transformative Power of Digital Governance in Global Governance'. You are expected to contribute to the entire program, but will primarily work within
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the bachelor’s program in Social and Cultural Analysis and the international master's program in Ethnic and Migration Studies. Research education and postgraduate education are mainly conducted in the areas
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sustainable future through materials science. Read more: https://wise-materials.org All early-stage researchers recruited into the WISE program will be a part of the WISE Graduate School https://wise
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for instance to socio-technological networks. The Division of Automatic Control has a strong commitment to WASP (Wallenberg AI, Autonomous Systems and Software Program) . This recruitment is potentially eligible
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, focusing on issues of technology and social change. Undergraduate programs include a bachelor's and a master's program in Urban and Regional planning, as well as courses in theory of science and history
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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