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challenges: Successful candidates will contribute to one or more of the following research domains: development of autonomous navigation and path planning algorithms for lunar terrain traversal and regolith
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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of the algorithms developed in this project. About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance
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responsibilities will be to: conduct high-quality research in intelligent sensing and control for complex project environments develop and implement AI-based algorithms (e.g., GNNs, deep reinforcement learning
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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data into clinical impact. They will be responsible for developing algorithms for image analysis, creating predictive models for disease progression, and identifying patterns in imaging data
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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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disease patients using radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical
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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time