27 natural-language-processing-intern Fellowship positions at Hong Kong Polytechnic University
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at the time of application; (b) have extensive experience in the field of natural language processing; (c) have good working knowledge of Python, Pytorch, Linux, Git, etc; (d) have a good command of
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proficient in the CARLA driving simulation platform and using various map application programming interfaces; (d) possess strong programming skills in Python, Java and other languages; and (e
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the University and Boeing research agreement. They will be required to: (a) develop machine learning-based image processing algorithms for surface condition recognition, defect detection, and digital feature
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in the research project - “The Hong Kong Polytechnic University - Convergent International Travel Co., Ltd. International Tourism Digital Intelligence Joint Innovation Lab”. Qualifications Applicants
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Department of Language Science and Technology Part-time Postdoctoral Fellow (Ref. 260226003) [Appointment period: twelve months] Duties The appointee will assist the project leader in the research
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/ Project Administrative Assistant , applicants should be a form five school leaver with 5 passes in HKCEE including English Language (at least Grade C if Syllabus A; Level 2 if results are obtained from 2007
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experience at the time of application; (b) have experience in conducting research, and skills in AI for modelling and optimization of industrial systems; and (c) be proficient in English. Applicants
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twelve months] Duties The appointees will assist the project leader in the research project - “Develop a vision-language model-based smart driving assistant for enhancing safety and convenience
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of both written and spoken English and Chinese, with fluency in Cantonese and Putonghua; (d) be able to communicate effectively with both technical stakeholders in AI and data, as well as non
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twelve months] Duties The appointees will assist the project leader in the research project - “A vision-based human digital twin modelling and scene understanding approach for adaptive and natural task