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sustainable... Department: Uppsala University, Department of Information Technology Work Locations: Number of offers: 1, Sweden, Uppsala universitet, Uppsala Research Field: Computer science Technology
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sense. The programme focuses on medical, defence and communication applications. One of the many activities in the POLARIS programme addresses research on GaN Microwave Monolithic Integrated Circuits
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PhD fellowship in Section for molecular ecology and evolution at the Globe Institute, Faculty of Health and Medical Sciences, University of Copenhagen We are offering a PhD fellowship in Population
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annotations are scarce or unreliable. Recently developed unsupervised learning methods allow to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 25-Jul-25 Location: Minneapolis, Minnesota Categories: Academic/Faculty Medical - Research Additional Information: 2
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also design medical research experimental protocol for both healthy control population and target patient population, apply for the protocol’s ethical approval (please check this website for more
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to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them for downstream tasks. In this project, you will develop novel unsupervised machine learning methods
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 9 days ago
applications, including minimally invasive medical procedures and targeted delivery [1]. Precise magnetic actuation requires the use of bottom-up fabrication techniques in which magnetic interactions are well
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual