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. The position is particularly suitable for candidates with strong skills in applied mathematics, control, physics, or electrical engineering, and an interest in dynamic systems and modelling. Experience in system
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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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this unique program! At Linköping University, we are announcing the position as DDLS PhD student in Data-driven precision medicine and diagnostics Data-driven precision medicine and diagnostics covers
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This PhD position is placed in the GreenPolChem-group (supervised by Associate Professor Peter Olsén). The GreenPolChem is located in the Pronova Chemistry Lab, at the Laboratory of Organic Electronics (LOE
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project. The project will also employ a PhD student at Lund University, focusing on the applied aspects of the project, whereas the focus for the advertised position is on the machine learning method
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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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- 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