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Field
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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Development Fund (ERDF) though the “Programa de Inovação e Transição Digital” (COMPETE2030), taking place at the Laboratory of Image and Video Technology of Faculty of Sciences of the University of Beira
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are developing a groundbreaking technique based on autofluorescence (AF) imaging and Raman spectroscopy for detection of positive lymph nodes during breast cancer surgery. The instrument will allow surgeons
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UiO/Anders Lien 21st October 2025 Languages English English English PhD Research Fellow in Deep learning for imaging Apply for this job See advertisement About the position Position as PhD Research
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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/faculties/natural-sciences/our-research/research-groups/cognition-in-complex- environments/ and the inter-disciplinary Stirling Vision and Image Processing Special Interest Group http://vip.cs.stir.ac.uk
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three references and their contact information A link to GitHub repos for relevant code (optional) Starting salaries will vary depending upon the qualifications and experience of the selected candidate
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magnetic resonance imaging (fMRI) and/or neurophysiological data Development and programming of behavioral and cognitive tasks; animal training and testing Planning, preparation, and participation in
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the Research Fellow Statute (EBI), by the Norms for Grant Assignment and Management within R&D Projects, by the Code of Administrative Procedure (CPA), all in their current wording, and by other applicable
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Editing of 2D and panoramic images and 3D processing Ensure the processing of photographed objects, in accordance with the defined technical specifications 4. Applicable Legislation and Regulations: Law No