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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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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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learning architectures, addressing both Parameter-Efficient Fine-Tuning (PEFT) regimes and full end-to-end fine-tuning workflows. Where to apply Website https://www.unimore.it/ Requirements Additional
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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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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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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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. Through continuous structural monitoring systems Through continuous structural monitoring systems and artificial intelligence algorithms (machine and deep learning, hybrid physical-data driven methods
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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
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge