1,421 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions in Singapore
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with a passion for teaching. Instructors are expected to excel at teaching and work closely with students. As we are expanding our course offerings, we seek versatile individuals who can teach
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and course materials. Evaluate module assessment components. Teach up to 240 contact hours per academic year. Qualifications A Bachelor’s or Masters’ degree in a relevant discipline. Candidates must
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on new initiative and emerging technologies. • Conduct or facilitate knowledge sharing. Qualifications • A degree in Computer Science, Computer Engineering, Information Technology or equivalent. • Relevant
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imaging, signal processing, or machine learning. Hands-on experience with ultrasound systems, particularly Verasonics platform is preferred. Familiar with programming languages and environments such as
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enhancements. Learn and apply automation tools to streamline processes, strengthen controls, and improve operational efficiency. Develop skills that have a direct impact on students’ educational journeys
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Alumni Learning Day, Nanyang Alumni Awards, PACE Forward Series, webinars, alumni surveys, outreach campaigns, and the annual “Update Your Particulars” exercise. Plan, produce, and manage end-to-end
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to groom the next generation of leaders, thinkers, 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
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invites you to join our team At NUS-ISS, we don’t just teach AI, we help organizations adopt it responsibly and effectively. You will be part of a vibrant community of educators, innovators, and industry
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of Lecturer / Senior Lecturer and teach inter-disciplinary courses in the College, in the areas of innovation & entreprenurship, digital literacy / AI, and / or systems thinking. Each inter-disciplinary course
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Master’s degree in psychology, cognitive science, human-computer interaction, communication science, or a related field. Background in some empirical research methods (quantitative, qualitative, or mixed