55 engineering-computation "https:" "https:" "https:" "https:" "https:" "Ulster University" positions at Linköping University
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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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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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technologies. The OEM group is part of the Laboratory of Organic Electronics (LOE) (https://liu.se/LOE ), an internationally renowned research environment comprising more than 150 researchers from diverse
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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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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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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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include teaching or other departmental duties, up to a maximum of 20% of full-time. Your qualifications You have graduated at Master’s level in Electrical Engineering, Computer Science, or Applied
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with experimental collaborators from The Royal Institute of Technology (KTH) and Stockholm University. Where to apply E-mail registrator@itn.liu.se Requirements Research FieldChemistry » Computational
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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