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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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) have high proficiency in English; (d) have good computer literacy, especially in the use of SPSS and MS Excel in data analysis; and (e) have good organisational, communication, and interpersonal
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- “Environment and conservation fund - converting waste plastics into value-added products: process design, simulation, and feasibility analysis”. Qualifications Applicants should: (a) have an honours degree
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computer science, maritime economic or a related field, preferably with a publication record in computer vision or maritime-related research; (b) proficiency in programming, including Python and Matlab, data
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independently within a team and be a good team player; (h) be highly motivated, able to handle multiple tasks and work under pressure; and (i) have sound knowledge of computer applications. Preference will
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- “Environment and conservation fund turning sewage sludge into green hydrogen and e-fuel with carbon-neutrality goals in Hong Kong: innovative process design, optimization, economic viability and environmental
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technologies such as React, Next.js, ES6 and HTML5/ CSS3; (d) a strong experience of server-side development such as using Node.js, MySQL, Mongodb and Linux operation; (e) experience in web services using
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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 processing technologies, especially for light alloys
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- “Monetizing used masks: innovative integrated process design, optimization, and commercial-scale comprehensive decision-making”. Qualifications Applicants should: (a) have an honours degree or an equivalent
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) optimise BIM software performance and address challenges, including model processing, data storage, and rendering efficiency; and (e) collaborate with AI algorithm teams to understand the business