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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena. Experts in
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computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning models for prediction and recommendation Job
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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Responsibilities: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety
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Responsibilities: Integrate and analyze large-scale multi-omics datasets (genomics, transcriptomics, epigenomics) to derive biological insights Apply statistical and machine learning models to identify cancer risk
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and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative optimization and Machine Learning models to address key challenges in the future airspace
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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diffusion models using path integral formulations. This project aims to advance quantum machine learning by: Designing a quantum counterpart of diffusion models; Leveraging path integral methods to model
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience