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coordinated by the University of Bergen, Norway. Supervisors: Prof. Toni Mancini Location: Sapienza University of Rome, Italy Duration: 3 years (with possibility of extension) Start date: August 2026 at latest
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Network Endotrain (Grant No: 101227148) and coordinated by the University of Bergen, Norway. Supervisors: Prof. Toni Mancini Location: Sapienza University of Rome, Italy Duration: 3 years (with
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the research groups active at the KU Leuven Ghent Campus. This funding call is open to all EU and International applicants. Keywords: Digital Image Correlation, Experimental and Computational Mechanics
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to the development of computer platforms that support the prevention, diagnosis and management of endocrine disease (endocrine digital twins), Participants will collaborate closely with other Doctoral Candidates and
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levels. Contributing to the development of computer platforms that support the prevention, diagnosis and management of endocrine disease (endocrine digital twins), Participants will collaborate closely
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Manufacturing and/or AI Robotics. Focus areas include: yield/defect causal analysis and XAI; process-variation monitoring and root-cause analysis; intelligent scheduling/dispatching for WIP/throughput (multi
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proposal. This PhD will evaluate the efficacy and suitability of digital image collection and analysis for beach litter characterisation on heavily-littered coastlines, in partnership with community groups
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experiments using MEA electrophysiology, live-cell imaging, and related techniques. You will study neuronal failure and recovery after metabolic stress in rodent and human iPSC-derived cultures, and evaluate
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for deployment and operation on a real-world quantum network prototype, build by the Quantum Internet Alliance (QIA) collaboration. The successful candidate enjoys experimenting with different approaches, and is
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strategy, the processing of sensor data (laserscans, images, pressure, etc.), as well as the reactive control of robotic arms. In order to apply for this position, the candidate should possess in-depth