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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Research and implementation of model-merging techniques for deep
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8 Nov 2025 Job Information Organisation/Company Università di Verona Research Field Computer science Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Country Italy Application Deadline 24 Nov 2025 - 13:00 (UTC) Type of...
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of deep neural networks in the field of adaptive processing of graph data (Deep Graph Learning) . The developed novel approaches will be applied to case studies in bioinformatics Requirements Additional
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environment to study these topics given its expertise in Machine and Deep Learning, Computer Vision, Signal Processing, and Multimedia. Also, its declared vision to work especially in presence of imperfect data
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/A (Logic and Philosophy of Science), research project "Critical History of Deep Learning". DEADLINE: February 2nd 2026, AT 1:00 P.M. CET Where to apply Website https://www.unive.it/data/50068/?id=2026
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efficient deep learning and support to teaching and outreach on sustainable and multimodal AI. Where to apply Website https://www.unimore.it/ Requirements Additional Information Eligibility criteria Eligible
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neurobiology, aging-related disorders, infectious diseases, immunology, oncology, cardiovascular diseases, and deep knowledge and expertise in at least one of these areas. Excellent networking and collaboration
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in industry or academia in a mid/senior or junior role. Documented experience in the development of modern machine learning pipelines, data pipelines and computer vision systems, using deep learning
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and image generation based on deep learning. The aim is to study techniques for handling multimodal data by integrating visual information (2D and 3D) with textual or tabular metadata. This integration