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Postdoc in Generative Machine Learning for Biomedical Data | Human Technopole, Milan Build the science that shapes the future of human health. Application closing date: 21.02.2026 Join a place where
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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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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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, exoskeletons and force augmentation, manipulation and dexterous manipulators, telepresence and teleoperation, industrial automation and robotics, active perception and learning, inspection robotics, hyper
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Computer-Assisted Laser Microsurgery,” IEEE Transactions on Medical Robotics and Bionics, https://doi.org/10.1109/TMRB.2024.3468385 , 6(4), pp. 1423-1435, November 2024 ESSENTIAL REQUIREMENTS PhD degree in
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Union within the project “A Comprehensive Trustworthy Framework for Connected Machine Learning and Secure Interconnected AI Solutions (CoEvolution)”, - CUP F23C24000210006 – selection code: ipd_10D_0426_09/IINF
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maintain end-to-end EO data processing pipelines, from sensor calibration to the extraction of geophysical variables. Implement machine learning and image processing techniques to fuse optical and SAR data
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dependable large-scale software systems, integrating expertise in: Software Engineering Machine Learning & MLOps Robotics & Cyber-Physical Systems Cloud & HPC ecosystems Interdisciplinary research. As a
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for testing machine learning algorithms applied to generative design in support of existing heritage regeneration processes. Where to apply E-mail reclutamento.docenti@ateneo.uniroma3.it Requirements Additional
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to perform correlative operando measurements Essential requirements PhD in Material Science, Chemistry or Physics or equivalent degree Hands-on experience in electrochemistry Hands-on experience in Raman and