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The Centre for Quantum Technologies at NTU invites applications for a Research Fellow position in Quantum-Enhanced Learning, Agents and Algorithms. The successful candidate will conduct independent
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 18 hours ago
. Learn more about ANU, our history and the community that has shaped us: ANU at 80 Position overview The Tax and Transfer Policy Institute (TTPI) is a strategic collaboration between the Australian
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following projects: Project #1 Title: Enhance Students' Interdisciplinary Learning through Large Language Model-Empowered Learning Analytics (MOE TRF 2/25 ZG) Project #2 Title: Building an Eco-system
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of Australia’s energy transition. As a Postdoctoral Research Fellow, you will contribute to the Grid Enhancement Technologies grants program funded by the Department of Climate Change, Energy, the Environment and
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced
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The Research Fellow (RF) will conduct research in the field of coastal dynamics, with a focus on the development and application of machine-learning-enhanced coastal models. The project aims
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Degree in engineering, computer science, physics, mathematics, statistics or related quantitative discipline (UK 2:1 or equivalent). Working understanding of modelling, machine learning techniques and
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behaviour and mindful of how it impacts on others, enhancing personal skills to adapt professional practice accordingly. Delivering excellent service Providing the best quality service to external and
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education. The successful candidate will work at the intersection of child development, educational research, and emerging technologies to generate evidence that informs the age-appropriate use of AI
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Nanyang Technological University (NTU) invites applications for a Research Fellow position to support a MOE Tier 2 research project examining how Generative AI output design can enhance higher-order