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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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Engineering, go to: https://liu.se/en/article/open-positions-at-isy . The employment When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral
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biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in
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emissions and biodiversity impacts related to bank lending, particularly in agriculture. Therefore, banks therefore often must rely on coarse standard values or low‑precision models, making it difficult
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government-funded strategic research area in IT and Mobile Communications. Read more: https://elliit.se/ We have the power of over 40,000 students and co-workers. Students who provide hope for the future. Co
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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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7 Mar 2026 Job Information Organisation/Company Linköping University Research Field Engineering » Communication engineering Researcher Profile First Stage Researcher (R1) Application Deadline 20 Apr
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Technology, located on the river Motala Ström at Campus Norrköping. You can read more about Norrköping here: https://visit-norrkoping-se.translate.goog/bo-studera-och-etablera?_x_tr_sl=sv&_x_tr_tl=en&_x_tr_hl
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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