11 machine-learning-postdoc-"https:" Fellowship positions at Hong Kong Polytechnic University
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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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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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or an equivalent qualification. For all posts, applicants should also: (a) have solid experience in electric machines, finite element methods and theory of electromagnetic files; and (b) be able to complete
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the research project - “Advancing generative AI algorithms for haptic rendering: bridging human-computer and human-robot interactions”. Qualifications Applicants should have a doctoral degree or an equivalent
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. Preference will be given to those with a relevant portfolio related to system development, human-computer interaction or cultural studies, and/or with knowledge of Cantonese. Applicants are invited to contact
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in the project – “Self-paced learning platform powered by efficient graph retrieval-augmented generation”. Qualifications Applicants for the Postdoctoral Fellow posts should have a doctoral degree or
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Interaction, Interactive Computing, Product/Industrial Design or a related discipline and must have no more than five years of post-qualification experience at the time of application. Applicants
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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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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