55 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at Linköping University in Sweden
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conduct research on the theoretical foundations of mathematical optimization, as well as its applications to emerging challenges in machine learning and engineering. You will write and submit research
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and supportive Laboratory of Organic Electronics (https://www.liu.se/loe ). LOE currently has >150 researchers and research students across thirteen group sharing an open lab environment for fruitful
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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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machine learning. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three years. The employment is full-time. Starting date by
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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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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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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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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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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, or related areas, fields or environments. We expect experience and competences in one or more fields of research on late working life; labour markets; public, branch and employer policies; lifelong learning