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qualification; (b) experience in using machine learning for research projects; and (c) have a good command of both written and spoken English. Applicants are invited to contact Prof. Yoo Hee Hwang
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) have a good knowledge of computer vision and programming skills; and (c) be willing to learn research methods, data processing and data analysis. Applicants are invited to contact Prof. Lawrence W. C
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- “Deep learning-based approach for process parameter optimization of SiC wafer under limited data”. He/She will carry out research in the area of machine learning (ML) and data science, and also be
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and energy materials. Preference will be given to those with knowledge of computer programming, AI and/or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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, reports and presentations for stakeholders. Preference will be given to those who have proficiency in programming languages such as Python and familiarity with AI/machine learning frameworks (e.g. PyTorch
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ethnic minority children in kindergarten and junior primary sector to learn Chinese under a culturally responsive education framework, as a scale-up extension of a completed project called “C-for-Chinese
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committed to providing high-quality education in Artificial Intelligence (AI) for undergraduate students to keep status quo. These courses represent key initiatives to promote active learning and
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machine learning-enhanced algorithm incorporating domain knowledge for sustainable maritime transport”. Qualifications Applicants should have: (a) an honours degree, preferably in mathematics, logistics
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Professors/Associate Professors/Assistant Professors, Department of Computer Science and Engineering
machine learning Bioinformatics Computer system security or cybersecurity Data analytics or data science Healthcare Informatics Image processing, computer graphics, AR/VR Programming languages, software
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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole