53 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "UCL" "UCL" positions at Linköping University
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-assisted AI and control systems is to deliver the right and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you
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17 Mar 2026 Job Information Organisation/Company Linköping University Research Field Computer science Researcher Profile First Stage Researcher (R1) Application Deadline 13 Apr 2026 - 12:00 (UTC
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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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conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in-distributed-wireless-systems/ Distributed MIMO
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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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and Software Program). WASP | Wallenberg AI, Autonomous Systems and Software Program which is Sweden’s largest individual research program ever, a major national initiative for strategically motivated
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competitive advantage (https://liu.se/en/research/cbmi ). You will work under the supervision of Professors Christian Kowalkowski and Daniel Kindström. Research at IEI spans a broad range of areas, from
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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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include teaching lessons, supervising in the computer lab or during laboratory exercises, and other tasks, including correcting reports, quizzes, etc. About you To be employed as an amanuensis, you must
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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