20 postdoc-machine-learning Fellowship positions at Hong Kong Polytechnic University in Hong Kong
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health monitoring, preferably with a publication record in top-tier journals; and (c) be proficient in mainstream research frameworks for deep learning and computer vision. Applicants are invited
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machine learning methods, particularly large language models (LLMs), to marketing research. Applicants are invited to contact Prof. Edward Lai at telephone number 2766 7141 or via email at edward-yh.lai
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and energy materials. Preference will be given to those with knowledge of computer programming, AI or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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application; (b) experience in conducting human neuroscience research and/or be proficient in computer programming, e.g. Matlab and Python; (c) a good command of both written and spoken English; and (d
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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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research using methods such as sensing technique, 3D printing, human-computer interaction, simulation, and/or machine learning to address challenges in machinery motion planning and construction safety
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experience at the time of application; (b) have experience in InSAR applications for landslides and reclamations, PolSAR/PolInSAR for vegetation mapping, finite element analysis, numerical simulation, machine
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posts) [Appointment period: each for twelve months] Duties The appointees will assist the project leader in the research project - “Auto-calibrated real-time locating service-based production logistics
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will assist the project leader in the research project - “AI-based fashion video generation system”. They will be required to: (a) take part in design and carry out artificial intelligence, machine
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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