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project – “Universal time-series foundation model bridging power electronics reliability monitoring and grid-edge sensing” Qualifications Applicants should have: (a) a doctoral degree or an equivalent
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project – “AI-enhanced physics for advancing reliability validation in power electronics” Qualifications Applicants should have: (a) a doctoral degree or an equivalent qualification and must have no
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academic papers; (c) the ability and a good understanding of conducting data analysis; (d) an excellent command of spoken English; and (e) the ability to work independently and meet the work
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) hands-on experience in conducting experiments, data collection, and/or data processing and analysis or strong programming skill; and (c) the ability to assist in system and software development
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on application logic rather than underlying algorithm development), and the capability of independently driving the full "Data-AI-Deployment" process; (c) the ability to evaluate the fit between AI applications
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phenomena; and (e) demonstrated ability to publish research findings in high-impact international journals. Applicants are invited to contact Miss Samantha M. S. Sin at telephone number 3400 2543 or via
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recognition, or computer vision; (b) a good understanding of the fundamentals of robot control, cobots, or UAV path planning; relevant coursework or project experience is preferred; (c) strong hands
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background in 3D perception, including segmentation, localization, mapping and multi-modal sensor fusion; (b) proficiency in programming languages such as Python and C++; (c) demonstrated ability
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The appointees will assist the project leader in the research project - “A novel sustainable campus lighting and charging infrastructure managing flexible multi-source power generation”. Qualifications
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Department of Electrical and Electronic Engineering Part-time Research Fellow (Ref. 251110001) [Appointment period: eight to thirty-six months] Duties The appointee will assist the project leader in