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optimal performance The PhD student will be supervised by Ida-Maria Sintorn, Professor in digital image processing, and Jens Sjölund, Assistant Professor in AI at the Department of Information Technology
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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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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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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The research group A DDLS PhD position is available in the Engblom lab , with main
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Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes
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on primary brain tumors. The Wittrup group works with a range of techniques to improve RNA delivery to tumors, as well as developing methods to study and characterize these processes. Within the research group
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preparation, development and verification of models and generalization of solutions. Your background For this position, you are required to have a PhD in electronics, metrology, computer engineering or a
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technology Researcher Profile First Stage Researcher (R1) Application Deadline 14 May 2026 - 21:59 (UTC) Country Sweden Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research
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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