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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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application! We are looking for a PhD student in automatic control at the Department of Electrical Engineering (ISY). Your work assignments The research area for the position is complex networks and multi-agent
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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application! We are looking for a PhD student in automatic control at the Department of Electrical Engineering (ISY). Your work assignments You will work on a project on data driven control. In recent years
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of WWSC is to offer sustainable bio-based alternatives to current fossil-based materials. As a PhD member of WWSC, you will have access to a large research network across major universities in Sweden (KTH
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning
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- Linköping University The PhD position is funded by the Wallenberg Wood Science Center (WWSC). The long-term vision of WWSC is to offer sustainable bio-based alternatives to current fossil-based materials. As
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duties, up to a maximum of 20 per cent of full-time. Your qualifications To be employed as a PhD student you need to have completed a degree at Master’s level in Electrical Engineering, Computer
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning