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The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space creation and urban infrastructure development. The School of Civil
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Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations
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Responsibilities: The candidate will study theoretically forward and inverse uncertainty quantification problems for partial differential equations, and multiscale partial differential equations. He/she will develop
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on developing the low-dimensional nanophotonics, focusing on the near-field, nonlinear and quantum optical properties of emerging low-dimensional materials. Key Responsibilities: The Research Fellow will work
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Responsibilities: Conducting regular field work to collect seismic data in Indonesia and Singapore. Seismic data processing and analysis. Implement multiscale subsurface imaging and inversion algorithms
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distributed energy resources (DERs). Design & develop optimization algorithms/tools to plan the deployment of DERs such as energy storage systems (ESS), photovoltaic generations (PV), electric vehicle charging
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Responsibilities: Research and develop novel ML-based methodologies and algorithms in LLM-empowered Sub-Graph Learning for Large Graph Models. Working closely with other Postdoc/RA/PhD students to discuss the ideas
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leverage their expertise to develop innovative algorithms for data analysis. Additionally, they will be responsible for communicating their findings to the scientific community through academic meetings and
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optimal output regulation for uncertain multi-agent systems”. The role of this position includes: Developing novel learning-based methodologies to address the prescribed-time control problem for uncertain
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research in MAE addresses the immediate needs of our industries and supports the nation’s long-term development strategies. In the new era of industrial 4.0 and sustainable living, MAE is rigorous in