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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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to support the research for the project titled: Automated IC Recognition and Verification from PCB Images. The objective is to design and develop computer vision techniques to analyse multi-modal PCB
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team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research
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team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research
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to InSAR and other remote sensing observations. Successful candidates will have a Bachelor’s degree in a relevant field (e.g., earth science, civil / electrical engineering, image processing, and deep
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movement skills. The experimental protocol will involve participants performing different motor tasks, while the researchers will collect multimodal data (i.e., movement, brain imaging) to dive into what is
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on a new indoor soundscape project with a speaker company. The role will focus on designing, building, and testing a working prototype of an indoor soundscape augmentation device. Key Responsibilities
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Responsibilities: Identify the weakness of existing watermarking and adversarial protection methods Develop new deep learning algorithms or improve existing algorithms to address the weaknesses. Prepare codes and
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of optomechanical and imaging projects. The role will focus on the manufacture and design of optomechanical assemblies. Key Responsibilities: Manage the end-to-end effort for the design and production