48 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Hong Kong
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Major with the Department of Economics. The Department hosts an MSocSc Programme in Comparative Social Policy (International) (IMCSP) and has been successful in attracting MPhil and PhD students from
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) and common data formats (e.g. RVT and DWG). Preference will be given to those with experience in computer graphics, 3D geometric algorithms or WebGL development. Applicants are invited to contact Prof
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The appointees will assist the project leader in the research project - “Research centre for non-invasive brain computer interface”. Qualifications For the post of Postdoctoral Fellow, applicants should have a
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using deep-learning-based spatial-acoustic imaging”. Qualifications Applicants for the Postdoctoral Fellow post should have a doctoral degree or an equivalent qualification and must have no more than five
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Mechanics and Fluid Mechanics; (c) research experience in computational fluid dynamics (CFD); and (d) experience in handling publications in the field of Fluid Mechanics. Preference will be given
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positions are possible if suitable candidates can be identified. The candidate is expected to have a PhD degree in astrophysics or related principles and will work with Prof. Bing Zhang to conduct research in
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for the Postdoctoral Fellow post should have (i) a PhD degree in Physics, Materials or Engineering and must have no more than five years of post-qualification experience at the time of application; and (ii) at least
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twelve months] Duties The appointees will assist the project leader in the research project - “Develop a vision-language model-based smart driving assistant for enhancing safety and convenience
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in five HKDSE subjects of Level 2 in New Senior Secondary subjects / "Attained" in Applied Learning subjects / Grade E in Other Language subjects, and the five subjects must include English Language
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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