51 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions at Linköping University in Sweden
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together with Jendrik Seipp, Senior Associate Professor in Artificial Intelligence at LiU. The research projects for the advertised position will be in the areas of automated planning and machine learning
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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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(Sensor Informatics and Decision-Making for the Digital Transformation). Read more about the Competence Center here: https://liu.se/forskning/seddit . The focus of this specific PhD project is to explore
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broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes
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application! We are looking for a PhD student in automatic control at the Department for Electrical Engineering (ISY). Your work assignments This PhD position is part of the ELLIIT research program (https
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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of 20 per cent of full-time. Your qualifications You have a master’s degree in electrical engineering, engineering physics, mechanical engineering, computer engineering, engineering mathematics or have
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well as the programmes in statistics, cognitive science and innovative programming. Read more at https://liu.se/ida The position is based at the Division of Statistics and Machine Learning (STIMA). We conduct 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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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials