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- XIAN JIAOTONG LIVERPOOL UNIVERSITY (XJTLU)
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science, artificial intelligence, computer vision, mobile robotics, machine learning, data science and analytics, or be able to demonstrate an equivalent professional practice and engagement. Previous experience in
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that blend industrial design with advanced technologies. He/she will apply expertise in areas such as electronics/sensors technology, machine learning/programming, physical ergonomics/cognitive ergonomics
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] Subject Areas: Statistics and Data Science(including Machine Learning, Actuarial Science, Mathematical Finance, Bioinformatics, Artificial Intelligence etc) Appl Deadline: none (posted 2025/10/13 05:00 AM
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administration or data analytics, such as Applied Psychology, Organisational Behaviour, Statistics, Machine Learning, and so on; Excellent oral and written communication skills in English, including the ability
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China invites applications for a Professor / Associate Professor in Econometrics with a strong specialization in Machine Learning and Data Science. The appointment is expected to begin in August 2026
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: Unity, Unreal) Fundamentals of Machine Learning, data mining, and Big Data analytics Neuroscience for Education (EEG, EMG, GSR, EDA, HR, ST, RR, HRV, BPR, etc.) Image and video processing Technological
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+ AI framework, and the continued development of new academies to build an institutional ecosystem. With a focus on innovative learning and teaching, and research, XJTLU draws on the strengths of its
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Vision, Machine Learning and Aritificial Intelligence, and Human-Centered Computing. RESPONSIBILITIES Applicants will have an adequate level of teaching experience, have published high-quality papers in
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fields of computer science, data science, artificial intelligence, machine learning, deep learning, computer vision, natural language processing, biocomputation, nerual networks, generative artificaial
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project management. Areas of expertise in Department of Intelligent Science: Natural language processing, Machine translation, Information extraction, Machine learning, Computer vision, Pattern recognition