10 machine-learning-postdoc-"https:" "Naturalis" Fellowship positions at Hong Kong Polytechnic University
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challenge issues, using advanced machine learning models and necessary techniques; (d) evaluate and validate the performance of proposed methods and algorithms through theoretical analysis; (e) maintain
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-inspired learning algorithms for efficient, robust and scalable pattern recognition; (b) assist in general management of the project; and (c) perform any other duties as assigned by the project leader
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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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factors engineering, human-computer interaction and design; (c) user research experience and proficiency in using qualitative and quantitative research methods; (d) interest and experience in research
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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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command of both written and spoken English and Chinese; (c) have good computer literacy including MS Word, Excel, PowerPoint and Chinese word processing; (d) have good interpersonal, communication and
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twelve months] Duties The appointees will assist the project leader in the research project - “Towards multimodal seamless human-robot collaboration: few-shot perception and spatial skill learning with
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of artificial intelligence in language teaching and learning. He/She will be required to: (a) conduct independent and collaborative research on individual differences and second language acquisition; (b
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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