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on learning efficiency and credit attribution effectiveness. Job Requirements: Preferably Bachelor’s degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly
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of machine learning, simulation-driven testing, and iterative calibration based on real-world datasets. Contribute to scholarly publications, technical documentation, and progress reports required by funding
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for real-time, bidirectional data exchange between DT and EMS, incorporating secure data pipelines and resilient middleware. Improve predictive performance and operational robustness through machine learning
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science. Apply and/or develop cutting-edge computational methods, including machine learning and AI, to address fundamental questions in biology, medicine, or public health. Contribute to undergraduate and
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) into machine learning models. Analyze and interpret experimental results, ensuring rigorous validation and real-world applicability. Prepare and publish high-impact research at top venues (e.g., S&P, USENIX
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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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processing and machine learning. We regret to inform that only shortlisted candidates will be notified. Hiring Institution: NTU
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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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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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Responsibilities: Conduct research in the domain of real-time scheduling and resource allocation problems for machine learning pipelines deployed in safety-critical cyber-physical systems. Provide implementation and