48 engineering-computation "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at Linköping University
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for candidates with a Ph.D. in Electrical Engineering or equivalent, a strong mathematical background and a strong publication record in journals relevant to the research field. As the university operates in
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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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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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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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workplace The Division of Industrial Management at the Department of Management and Engineering (IEI) conducts research and provides undergraduate, postgraduate, and doctoral education in marketing and
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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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Management Lab (KDMLAB) of the Department of Computer and Information Science at Linköping University. The department is one of the largest computer science departments in northern Europe, with research
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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