19 modal-analysis-machine-learning Fellowship research jobs at Hong Kong Polytechnic University
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Assistant, applicants should have an honours degree or an equivalent qualification. For all posts, applicants should have strong background in materials engineering, machine learning or AI-related fields
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- “Development and application of intelligent localization and mapping system for autonomous underwater robots based on multi-modal fusion”. They will be required to: (a) conduct advanced research on multi
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Fuel life cycle analysis (LCA) and techno-economic analysis (TEA), they will be required to conduct life cycle environmental impact assessments of sustainable aviation fuels and other energy sources
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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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) strong background in quantitative methods, statistics, computer science, geospatial data analysis and modeling; (b) experience in AI and geospatial computer version; (c) advanced skills in scientific
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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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. 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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management. They will be required to: Post 1 of Research Assistant (a) conduct mechanical design and analysis for innovative space applications; (b) source and select components for research prototypes
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- “Fiber route estimation for optical networks in metropolitan areas based on DAS signal analysis”. Qualifications Applicants should have a doctoral degree plus at least three years of postdoctoral research
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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