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background/interest in time-series analysis, theoretical machine learning on networks, and high-dimensional statistics. Key Responsibilities: Take the lead in developing sub-projects (problem formulation
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of the project in top tier conferences and journals Key Competencies and Requirements: PhD in Electrical and Electronic Engineering/Computer Engineering/Computer Science or related engineering degree At least 1
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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acquired from the field survey Develop machine learning models for prediction and recommendation Job Requirements: Preferably PhD in Computer Science or related field. Expertise in computer programming
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. Job Requirements: PhD in Electrical and Electronic Engineering, Computer Science, or related field, with strong background in robotics and machine learning. At least 4 years of research experience in
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necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets. Collaborate with multidisciplinary teams including biologists, engineers, and clinicians
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the future of automobile manufacturing. The Hyundai-NTU-A*STAR Corporate Lab invites applications for the position of Research Fellow. Key Responsibilities: Research & Development of advanced innovative
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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