19 machine-learning-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" Fellowship positions in Hong Kong
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representation for multi-model fusion”. 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
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more than five years of post-qualification experience at the time of application; and (b) strong background in accounting, finance or computer science/natural language processing/machine learning
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, machine learning systems, model merging/fusion or related areas; (b) strong self-directed learning ability; and (c) commitment to open, reproducible and ethically responsible AI research. Applicants
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the project leader in the research project - “Novel electrical machines and drives”. Qualifications Applicants should have: (a) a doctoral degree or an equivalent qualification and must have no more than
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proficiency in machine learning, statistical modeling, and data analysis using Python, R, or similar platforms. Experience in grant proposal writing, scholarly manuscript preparation, and psychological
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project - “Financial support for book writing”. Qualifications Applicants should have: (a) a doctoral degree in Computer Engineering or a related discipline plus at least six years of postdoctoral
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of permanent magnet machines and control systems. Applicants are invited to contact Prof. Z.Q. Zhu at telephone number 2766 6257 or via email at z.q.zhu@polyu.edu.hk for further information. Conditions of
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and energy materials. Preference will be given to those with knowledge of computer programming, AI and/or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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, Computer Engineering, Electronic Engineering, Automotive Engineering, Aerospace Engineering or a related discipline plus at least six years of postdoctoral research experience or equivalent qualifications
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project - “Towards efficient large model training via learning from small model”. He/She will be required to: (a) conduct research on knowledge distillation and transfer learning from small models