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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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, machine learning, and life cycle assessment, we aim to create sustainable wearable systems to enhance human well-being. For more details, please view https://www.ntu.edu.sg/mse/research . We are seeking a
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that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes vulnerable groups
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problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems. Key Responsibilities Participate
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking to hire a research fellow (postdoc) to undertake a project to develop efficient AI and Machine
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proficiency in machine learning, statistical modeling, and data analysis using Python, R, or similar platforms. Experience in grant proposal writing, scholarly manuscript preparation, and psychological
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, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune microenvironment architecture. Collaborate with wet lab
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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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design and implementation of visual cues detection on video datasets (face and gesture), context-aware multimodal analysis, machine learning/self-supervised learning for automatically detecting subtle
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of childhood allergic diseases. The candidate will develop machine learning–based biomarker prediction models to identify microbiome-derived signatures associated with allergy risk and immune tolerance outcomes