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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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to demonstrate documented proficiency in English. You have knowledge and expertise in computer vision and/or medical image analysis, deep learning as well as mathematics. You have substantial expertise in
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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will
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, and second-life applications, ensuring both scientific impact and industrial relevance. As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part
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curiosity, independence, and teamwork. More information about the division is available at: https://liu.se/en/organisation/liu/isy/rt . For more information about working at the Department of Electrical
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collaboration and information sharing. You will formally be part of LOE’s Organic Bioelectronics group (liu.se/en/research/organic-bioelectronics ). Your work will primarily be under the guidance of Assistant
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://liu.se/en/research/remeso/research-fields Your qualifications You have graduated at Master’s level in the Social Sciences or Humanities or completed courses with a minimum of 240 credits, at least 60 of
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Engineering (ISY), Linköping University. The Division for Communication Systems carries out research, undergraduate and postgraduate education in communications engineering, statistical signal processing
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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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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential