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Position Description The Institute of Natural Sciences (INS, https://ins.sjtu.edu.cn/ ), Shanghai Jiao Tong University, is an interdisciplinary platform for conducting first-rate research, education
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competitions. Excellent communication skills in English; Chinese language skills are a plus Industry experience preferred Must have a research background in Artificial Intelligence, Machine Learning, or Deep
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Computer Science and Technology/Artificial Intelligence (Ref: FST250903) Job Description Candidates with expertise in one or more of the following areas: Artificial intelligence, computer vision and pattern
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Position Description The Institute of Natural Sciences (INS, https://ins.sjtu.edu.cn/ ), Shanghai Jiao Tong University, is an interdisciplinary research institute with special strength in applied
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fluid mechanics, computational geometry, meshing, computational graphics, computational vision, or scientific machine learning in general. Successful candidates will join a community of researchers in
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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bring together four existing departments from the Faculty of Science and Technology, including the Department of Civil and Environmental Engineering, the Department of Electrical and Computer Engineering, the
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Engineering Computer and Data Engineering Electronic and Electrical Engineering Information Engineering Microelectronics Engineering Multimedia Information Technology. Major Responsibilities The successful
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of interdisciplinary AI and data science are also encouraged to apply: (1) AI for Science, including AI/ML for scientific discovery, foundation models for science, scientific machine learning, and AI applications in
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Qualifications Master’s degree or above in Electrical Engineering, Electronic Engineering, Embedded Systems, Computer Engineering, Automation, or a closely related field. Strong background in embedded systems