82 information-security-"https:"-"https:"-"IFM"-"IFM" Fellowship positions at Hong Kong Polytechnic University
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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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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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for the Senior Research Fellow post should have a PhD degree in Computer Science, Artificial Intelligence, Data Science, Digital Economy or a related field plus at least six years of postdoctoral research
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smart technologies encourage or inhibit self-guided senior international travelers? The impact of new smart technologies on their technostress and purchase decisions”. He/She is required to: (a
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editing”. Qualifications Applicants for the Postdoctoral Fellow post should have a PhD degree in Computer Science, Electrical and Computer Engineering or a related discipline or an equivalent qualification
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. Applicants are invited to contact Dr Zhang Huan via email at zhanghuan.zhang@polyu.edu.hk for further information. Conditions of Service The remuneration rate is HK$385 per hour. Consideration of applications
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reviews; (b) develop and conduct writing tasks and questionnaires; (c) assist in data coding and basic data analysis; (d) assist in managing the project; (e) assist in preparing progress/final
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the research project - “Advancing generative AI algorithms for haptic rendering: bridging human-computer and human-robot interactions”. Qualifications Applicants should have a doctoral degree or an equivalent
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at the time of application. Applicants are invited to contact Prof. L. T. Hsu at telephone number 3400 8061 or via email at lt.hsu@polyu.edu.hk for further information. Conditions of Service A highly
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posts) [Appointment period: each for two months] Duties The appointees will assist the project leader in the research project - “Data-driven decision making using parametric distributionally robust