621 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Newcastle University" research jobs in Singapore
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an emphasis on technology, data science and the humanities. We are seeking accomplished and highly motivated Research Assistant/Associate to join an Industry Alignment Fund - Pre-Positioning (IAF-PP) grant
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Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a
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count data, offering a rare opportunity for applied research and experimentation. The postdoctoral research fellow will be supervised by the principal investigator Dr. Adrian Chong from the Department
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reactions, or transport mechanisms. • Experience in data-driven modeling or machine learning applied to membrane materials or separation performance analysis is a strong advantage. • Candidates with strong
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, conferences and ARI social events. Other benefits that the University provides and information about working at NUS and living in Singapore are available at Why Join Us . Terms and conditions, according
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quantitative data and statistical analyses (preferably with health-economic data) with modelling packages such as STATA. Proficient with Microsoft Office Suite. Possess strong verbal and written communication
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an emphasis on technology, data science and the humanities. Our research focuses on uncovering the cellular, molecular and metabolic mechanisms driving cardiovascular and skeletal diseases. We are a dynamic
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given - Perform microscopy and nanodevice analysis Program and design PLC systems Apply Computer Vision for machine hardware feedback and control Generate technical reports, white papers, and
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, patents, and publications. Job Requirements: PhD in Computer Science, Information Security, Engineering, or a related discipline. Research experience in software/system security, fuzz testing, or embedded
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation