14 learning-"https:"-"https:"-"https:" Fellowship positions at Hong Kong Polytechnic University
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Research Institute for Federated Learning Postdoctoral Fellow (Full-time/Part-time) (two posts) (Ref. 260929011) [Appointment period: each for twelve to twenty-four months] Duties The appointees
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to two years of research experience in visual computing, deep learning, and/or brain-inspired circuit and system design, and must have no more than five years of post-qualification experience at the time
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learning models and are familiar with foundation models. Applicants are invited to contact Dr Ren Ge Gary at telephone number 3400 8595 or via email at [email protected] for further information
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– “A human-like progressive learning paradigm for embodied AI: Meta skills for robots by watching and imagination”. They will be required to: (a) identify irreducible low-level sensorimotor units
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- “Reliable industrial foundation models for trustworthy industrial robot fault diagnosis in automotive smart manufacturing”. He/She will be required to: (a) carry out research in advanced machine learning
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Secondary subjects/“Attained” in Applied Learning subjects/Grade E in Other Language subjects, and the five subjects must include English Language, Chinese Language and Mathematics. Applicants are invited
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the project leader in the research project - “Empowering language education in the AI era: Developing students’ self-regulated learning skills in English speaking”. He/She will be required to: (a) conduct
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-science and signal-analysis tasks, including processing experimental signals, integrating datasets, developing machine-learning models, and mapping measured fuel properties to SAF performance. For the post
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of environmental factors of 60,000 subjects across multiple time points. Our research laboratory has great computing capacity, including multiple H100 and A100 GPU systems for deep learning, and computing clusters
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The appointee will assist the project leader in the research project - “Enabling secure and resilient distribution grid operations through physics-informed topology learning and coordinated network-load