650 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions at Nanyang Technological University
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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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research team as a Research Engineer. The successful candidate will support ongoing research initiatives by applying advanced machine learning, NLP, and large language models (LLMs) to develop next
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, building automation and control (BAC) system, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced
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engineering or related field At least 1 year of relevant experience in signal processing and machine learning. Good written and oral communication skills Proficiency in ANSYS, and lab test skill Ability to work
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a Research Fellow to contribute to a project focused on focused on data-driven discovery of atomic catalysts. Key Responsibilities: Theoretical predictions using DFT and machine learning, and
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Manage day-to-day operations of the cohort management, including interactions with programme partners, instructors, venture builders, founders, and mentors. Coordinate learning modules, expert sessions
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tools (e.g. RPA, intelligent document processing, workflow automation) Experience designing dashboards and reports using modern BI tools (e.g. Power BI) Working knowledge of AI / machine learning concepts
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of lab-scale and pilot-scale experiments/operations with element of machine learning, sample characterizations, data analysis, and so on. Job Requirements: PhD degree in Materials Science, Polymer Science
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digital humanities, ludic studies, or social gaming platforms. A passion for education and the ability to foster a supportive, inclusive, and diverse learning environment, with strong interpersonal and
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. Experience with data-driven modelling, machine learning, or AI applications in energy systems is an advantage. Familiarity with modelling of energy networks, district cooling systems, or integrated urban