645 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Nanyang Technological University
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work in strategic directions that are of significant impact to industry, science and technology. For more details, please view https://www.ntu.edu.sg/cee We are looking for a Research Engineer I to join
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and co-curriculum. Hence, NTU launched a new curriculum structure in 2021 featuring a range of ICC Courses. Learn more about the ICC Courses here: https://www.ntu.edu.sg/education/inspire
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and professional growth. Please refer to https://www.ntu.edu.sg/nie/ for more information on NIE courses/programmes. Requirements A Master’s degree, preferably in Counselling and Guidance or Applied
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innovation. The Platforms Engineering Group builds and operates the infrastructure and systems that enable AI practitioners across AISG's programmes to develop, train, and deploy machine learning models
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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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(ALC) Department. Job Description Part-Time Lecturers are engaged for teaching duties such as conducting lectures, tutorials and practicum supervision. Please refer to https://www.ntu.edu.sg/nie
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documentation. Applied Machine Learning: Possess deep, practical knowledge of machine learning fundamentals, with proven experience applying algorithms to solve problems in areas like NLP, Computer Vision, or
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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related to generative design. The key responsibilities include the following: To independently undertake research in machine learning. To publish high-quality research papers as required by the funding body
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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