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Sintorn, Professor in digital image processing, at the Department of Information Technology and conducted alongside researchers developing computational methods with a particular focus on deep learning and
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-future-green-electronics , https://c3nit.se/ Subject description The subject of this project is to develop novel ultra-wide bandgap semiconductor technology based on AlN and related alloys. This includes
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radioactivity. Important fields of application are radiotherapy to treat cancer, radiation diagnostics using X-rays, computed tomography, nuclear medicine and magnetic resonance imaging (MRI). We mainly work
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environment with wide-ranging expertise spanning data-driven imaging, clinical science, molecular biology, bioinformatics, and biomedical engineering, all working together to improve atherosclerotic and
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medical technology? Are you excited about achieving scientific breakthroughs together with talented and friendly colleagues? Are you interested in a research career in academia? Description of the workplace
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. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
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institution affiliated with both the Faculty of Engineering (LTH) and the Faculty of Science at Lund University. Within the division of Computer Vision and Machine Learning, there are several senior researchers
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Familiarity with large language models or multimodal systems An interest in visual reasoning, educational technology, or human–AI interaction Experience with neural networks for image or video understanding
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) classification and utilization based on advanced AI technologies, such as regenerative AI, image processing and reinforcement learning, that can improve the energy efficiency and reduce the operating cost and
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Project description Third-cycle subject: Biotechnology We are seeking a motivated PhD student to join our lab at KTH/SciLifeLab in Stockholm, focusing on precision medicine and cancer progression