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leverage their expertise to develop innovative algorithms for data analysis. Additionally, they will be responsible for communicating their findings to the scientific community through academic meetings and
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, SuperGLUE, LiveBench). Familiarity with distributed training frameworks and high-performance computing environments. We regret that only shortlisted candidates will be notified. Hiring Institution: NTU
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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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https://dr.ntu.edu.sg/entities/person/easonchen . We are looking for a highly motivated Research Fellow to facilitate research in using atom probe tomography (APT) to study the distribution of hydrogen in
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on rapid and accurate quantification of disasters using remote sensing and space geodesy. They will also advance InSAR processing algorithms to optimise change detection capability in Southeast Asia, where
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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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optimization problems Develop mathematical modeling framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data
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(OpelRT or Typhoon), electrical system design for better efficiency and system resiliency, and energy management algorithm development using MATLAB/Simulink for marine microgrid applications. Knowledge
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learning algorithms (Deep learning, Reinforcement learning, etc.); Proficiency in written and spoken English - essential for data analysis and communication with stakeholders Excellent oral communication
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aquaculture (e.g., behavioral analysis, growth prediction, digital twin, computer vision.) Develop, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets