137 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"https:" positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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, identify capabilities needed from the industry ecosystem, IHLs and centres. Involve in industry engagements, help to bring in higher value-added projects for the centres in SIT, Polytechnics and ITE. Applied
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multiple domains—FMECA, sensing technologies, data fusion, data analytics, and machine learning. Combining these diverse skill sets to develop a unified and reliable solution can be technically demanding
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degree or higher in Robotics, Computer Science, Mechanical Engineering, Control Engineering, or a related field. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. Working
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step change improvements in process performance; Informatics for process and product development with a background in big data applications in biotechnology, process development or food technology
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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of subjects such as computer vision, Machine Learning, Artificial Intelligence and Kinematics and dynamics. Autonomous systems, Robotics and Automation. Industry 4.0 and Internet-of-Things. Advantageous to have
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checking), quantum computing, and software analysis/verification. Have a degree in computer science, computer engineering, electrical engineering, or related areas. Possessing a Master degree will be
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related laboratories and tutorials for undergraduate students. Interview and supervise student assistants in the project. Job Requirements: (Research Fellow) PhD in Computer Science or a related field
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partners, including labs at the College of Computing and Data Science (CCDS) at NTU. Technical skills, natural curiosity, versatility, and enthusiasm are highly desired traits that will enable success in
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Requirements PhD in Geography, GIS, Remote Sensing, Environmental Science, Earth Science, or related disciplines. Strong working knowledge of GIS platforms (e.g., ArcGIS, QGIS) and spatial data analysis