24 computer-engineer "https:" "https:" "https:" "Eindhoven University of Technology (TU" PhD positions at Linköping University
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-time. Your qualifications You have graduated 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
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Technology, located on the river Motala Ström at Campus Norrköping. You can read more about Norrköping here: https://visit-norrkoping-se.translate.goog/bo-studera-och-etablera?_x_tr_sl=sv&_x_tr_tl=en&_x_tr_hl
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application! The Wallenberg Wood Science Center (WWSC) is a major research initiative between three leading Swedish universities: KTH Royal Institute of Technology, Chalmers University of Technology, and
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advances research on the changing material conditions of media, technology, culture and heritage, and how they intersect with environmental, institutional, industrial, and social conditions. Research in
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induction. You will combine advanced genetic engineering approaches with survival assays, fluorescence-based techniques in fixed and live cells, single-cell sequencing, and computational bioinformatics
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postgraduate education within Medical Science. The employment When taking up the post, you will be admitted to the program for doctoral studies. In connection with your admission to the doctoral program, your
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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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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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/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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, 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