10 machine-learning-"https:"-"https:"-"https:"-"https:"-"U.S"-"TCAT-Dickson" Fellowship positions at Hong Kong Polytechnic University in Hong Kong
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leader and contribute to the development of research activities; (b) develop reliable machine learning and generative AI algorithms for haptics applications; (c) document the steps of computational
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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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leader in the research project - “Control of permanent magnet machines for improving system efficiency of domestic air-conditioners”. Qualifications Applicants should have a doctoral degree or an
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- “Towards digital biomanufacturing – developing physics-informed machine learning framework for the advanced multi-modular 3D bioprinting system”. Qualifications Applicants should have: (a) a doctoral
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experience in conducting research in ultra-precision machining field, especially in polishing or surface/subsurface characterization; and (c) demonstrated by publication in top-tier journals. Applicants
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years of post-qualification experience at the time of application; (b) experience in using machine learning for research projects; and (c) have a good command of both written and spoken English
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. For the post of Research Assistant, applicants should have an honours degree or an equivalent qualification. For both posts, applicants should have relevant research experience in ultra-precision machining
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twelve months] Duties The appointees will assist the project leader in the research project - “A multimodal intelligence-enabled strategy learning approach for cognitive human-robot collaborative assembly
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of the project is the application of artificial intelligence in language teaching and learning. He/She will be required to: (a) conduct independent and collaborative research on individual differences and
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twelve months] Duties The appointees will assist the project leader in the research project - “A multimodal intelligence-enabled strategy learning approach for cognitive human-robot collaborative assembly