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Field
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algorithms for analyzing electrocardiography, electromyography and movement signals, identifying characteristics and recognizing patterns in everyday activities. Testing and validation of methods developed in
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17172 (COMPETE2030-FEDER-00864900) co-funded by the ERDF - European Regional Development Fund through Innovation and Digital Transition Program - COMPETE 2030 under the scope of Portugal 2030 and by
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insurance, supported by INESC TEC. 2. OBJECTIVES: Study the state-of-the-art in space robotics, focusing on navigation, control, and gas propulsion systems.; • Develop navigation, detection, and localization
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or international conference. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Research and development of algorithms for analyzing signals acquired in real time by a system with integrated
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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at the John Curtin School of Medical Research (JCSMR), within the ANU College of Science and Medicine. This role will lead the development of advanced deep learning frameworks—including graph neural networks
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
breakthroughs into commercial healthcare solutions. In the ICUSafeNotes project, we are conducting research and development of multimodal machine learning models that includes ICU note summarisation and analysis