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Description 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 to
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Proficiency in GUI development (e.g. using Python Dash , Tkinter , or web-based frameworks ) Experience working with data-driven simulation and model validation in power grids (Ph.D level is mandatory
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is looking for a Research Fellow to conduct research on AI-enabled discovery and development of advanced functional materials and coating processes, with emphasis on data-driven modelling, uncertainty
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management policies for an ageing population. In addition to utilizing road crash data, the project will also collect and incorporate driving simulator data to support model development and validation
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-based application development). Experience in handling real-world datasets and model development/deployment workflows. Domain expertise in building construction/civil/structural engineering, including
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well as practical experience in dynamic model development and the analysis of large-scale biological data, who are excited to apply rigorous computational modelling approaches to real biological and
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Engineering invites applications for the position of Research Fellow (LLM for Design-for-Safety). The successful candidate will join the research project “Multimodal LLM-driven Intelligent Design-for-Safety
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(NUS), and BEARS. The role will focus on developing and implementing AI/ML-driven high-throughput, closed-loop experimentation for the discovery and optimization of electrocatalytic materials for CO
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) environments, GPU-enabled systems, and cloud-based resources for large-scale data analysis and model development. Using these rich data resources, we develop and apply advanced AI/ML methods to model tumor
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conditions, which can be used to develop a data-driven model for storm surge prediction. Data Analysis: Process and analyze climate, ocean wave and coastal data to improve model accuracy. Research