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demonstrators. The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies
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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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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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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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://www.naiss.se/) , and a strong internal and international collaborative environment, the applicant is expected to establish and lead an independent research program in organic functional materials
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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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and activities connected to our PhD program. In addition to producing individual research, the applicant is also expected to interact with existing researchers within the department, and to contribute
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We have the power of over 40,000 students and co-workers. Students who provide hope for the future. Co-workers who contribute to Linköping University meeting challenges of today. Our fundamental values rest on credibility, trust and security. By having the courage to think freely and innovate,...
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qualification in a relevant subject e.g. bioinformatics, computer science or similar At least two years’ experience of working in a research environment with bioinformatics, genomics and interpretation of big
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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