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Requirements: PhD degree in Computer Science, Electrical & Electronic Engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine
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students. Assist in grant proposal applications. Requirements: PhD in Mechanical Engineering, Machine Learning, Artificial Intelligence, Computational Mechanics, Material Science, Industrial Engineering, or
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of marine geophysical survey and machine learning algorithms; Job Requirements: A Bachelor degree in geophysics or equivalent and A PhD degree in geophysics / geomechanics or equivalent from a recognized
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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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, Nonlinear Systems, Robotics, Machine Learning, Cyber-Physical Systems, Cyber Security, Internet of Things, and Computer Vision would be an added advantage. Fresh PhD graduates would be considered but
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discipline (e.g., computer science, data science, software engineering) Proficient in Machine Learning, Deep Learning, Python, Data Science, and AI/ML Toolkits Preferred Applicants with Master’s or PhD Degree
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program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and
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General Elective and will be offered to students taking NTU EEE MSc Communications Engineering, Computer Control & Automation, Electronics, Power Engineering, and Signal Processing & Machine Learning with
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
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Develop instrumentation and fixtures for the automation. Capabilities in advanced finite element or machine learning tools for process optimisation Disseminate the research outcomes into Journal