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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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28 Feb 2026 Job Information Organisation/Company Università di Pavia Research Field Medical sciences Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 30 Mar 2026 - 12:00 (UTC) Country Italy Type of...
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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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machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning
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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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Università Ca' Foscari Venezia - Dipartimento di Filosofia e Beni Culturali | Italy | about 1 month ago
/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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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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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