647 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "University of Waterloo" positions at Nanyang Technological University in Singapore
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Master’s degree in Electronic Engineering, Computer Science, or related field Knowledge of pinching antennas, wireless communications, and machine learning Strong analytical, research, and scientific writing
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PhD qualification degree in Electronic Engineering or Computer Science Familiarity with pinching antennas and machine learning Good written and oral communication skills Proficiency in python
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Requirements Master’s degree in Electronic Engineering, Computer Science, or related field Knowledge of pinching antennas, wireless communications, and machine learning Strong analytical, research, and
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implementation of data-driven computer vision and machine learning models using sensor data, camera feedback, and process parameters for print and tool path planning and process optimisation. Deploy real-time
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, modulation instability, and supercontinuum generation. Integrate experimental data with AI models, using machine learning to uncover hidden physics, accelerate simulations, and discover new operational regimes
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Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical
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, machine learning, and deep learning models. Key Responsibilities: Develop and apply time-series forecasting methods for semiconductor equipment health monitoring. Analyze equipment degradation data