49 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" "U.S" "U.S" positions at Linköping University
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application! We invite applications for a fully funded PhD student position to join the research group of Andrew Winters to work on challenging problems in Computational Mathematics for accurate and reliable
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. The research environment is embedded in Linköping University and closely connected to SciLifeLab and the national DDLS program. You can read about the workplace here https://liu.se/en/organisation/liu
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=sv 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 https://liu.se/en
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2026 - 12:00 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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at Master’s level in Computer Science, Electrical Engineering, or Applied Mathe- matics with a minimum of 240 credits, at least 60 of which must be in advanced courses in Computer Science, Electrical
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/tensions between the global North and global South. We will also consider applicants focused primarily on Swedish/Nordic cases or topics. For full information of the five REMESO research streams see: https
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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine
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Social Robots, which involves several Swedish universities and is funded by WASP-HS (https://wasp-hs.org/ ). The Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS) is a
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most two pages, explaining your motivation, research goals and why you fit the advertised position. The workplace The Department of Computer and Information Science was founded in 1983, but its roots go
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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy