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, such as, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, PINN/KAN models, neural operators, etc, and
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The project is to be carried out in collaboration with Schaeffler to study “collision monitoring and control system of cobot based on fiber optic sensing and deep learning”. Research Assistant
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The candidate will be expected to work on a project in collaboration with Schaeffler to conduct research on “collision monitoring and control system of cobot based on fiber optic sensing and deep
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in deep learning approaches and also experimental experience. Good communication and writing skills & team player. Strong interest in research work. Highly motivated, independent and resourceful. We
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equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written communication skills In-depth knowledge of deep learning, specifically, foundation models
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safer, more efficient, and sustainable mixed-traffic future. Key Responsibilities: Design and conduct driving simulation experiments for risk scenario analysis. Develop deep learning models, such as GNNs
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acoustic sensing leveraging deep learning To independently transform acoustic signals from continuous space to digital forms and apply math tools to interpret the results. To produce research reports and/or
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hands-on experience in areas such as computer vision, deep learning, multi-modal sensing, robotics, structural health monitoring, or digital twin technologies. (Fresh PhD graduates are welcome
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teamwork and verbal, written communication skills In-depth knowledge of computer vision and deep learning Demonstrated capability to conduct innovative research We regret that only shortlisted candidates
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original design requirements and specifications. Key Responsibilities: Deep drive into the modification of adhesives with suitable trigger chemicals and investigate the hysteresis heating response under