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in the area of distributed radar we are looking for a research fellow with expertise and experience in this area. Experience with highly accurate time synchronization of data received by a net of radar
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to empower recent Ph.D. graduates to conduct world-class research, publish in top-tier journals, and build a robust scientific network. By joining us, you will receive the mentorship and support necessary
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network of academic and industry experts shaping the future of smart shipping and maritime decarbonisation. As a university, we are committed to creating an inclusive environment that fosters respect
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to uncover SRL patterns in video-based learning (VBL) and digital game-based learning (DGBL) environments; • Conduct process mining and network analysis to differentiate SRL patterns between high- and low
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and beyond, and participating in research networks such as the Collections-Connections-Communities initiative. We particularly encourage applicants who will seek to bring new perspectives
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applications would be particularly beneficial, although not essential. In addition, experience in artificial intelligence, cloud computing, sensor networks and/or data visualisation would also be highly
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of classical and hybrid classical-quantum algorithms for treating the correlations. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research
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the electronic structure of these materials. This position offers exciting opportunities for collaboration within UQ, across the QDA network, and with external research partners. Key responsibilities will include
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research associates and PhD students. You will also engage directly with numerous external stakeholders (industry and academia), which will form part of your research network and give you opportunities
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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and