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The Continental-NTU Corp Lab is a strategic research collaboration between NTU and Continental, focusing on developing technically advanced solutions in the areas such as sustainable materials
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work on a project to conduct the research on the development of high-power motor system and the roles of this position include: Development of new motor design concepts for electric motor drives
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postdoctoral fellows to perform cutting-edge research in AI for radiation therapy. Research areas include developing and implementing AI techniques for image-guided radiation therapy, such as image
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: Computer Architecture Algorithms and Optimization Health Research Human-Computer Interaction Machine Learning and ML Foundations Machine Perception Natural Language Processing (including Information
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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
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) that supports the project titled Proposal and theoretical development of chip-scale chirped-pulse amplification systems. The RF will work on the project for the theoretical development of the chip-scale chirped
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on a project on Li-ion battery ageing analysis and experiment testing. Key Responsibilities: Perform experiment testing for Li-ion batteries Development of heath indicators based on the experiment
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transplantation. We are looking for a highly motivated candidate with a PhD degree interested in using microbiological methods with the aim of developing novel strategies to improve bone marrow transplantation and
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passionate individual contributing to NTU education environments A highly dynamic person inter-disciplinarily trained in AI, VR, and edu tech An experienced developer designing AI and VR for Education tools
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Dalhousie University | Halifax Mid Harbour Nova Scotia Provincial Government, Nova Scotia | Canada | about 10 hours ago
services. Specifically, we use large health administrative data and national population health data to develop risk prediction models to inform early identification of mental health problems, mental health