24 machine-learning-phd-in-denmark Fellowship positions at Hong Kong Polytechnic University
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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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). Qualifications Applicants should possess a PhD degree in Applied Linguistics, TESOL or a related discipline, or an equivalent qualification, and must have no more than five years of post-qualification experience
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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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for the project; and (h) perform any other duties as assigned by the Head of School, the project leader or their delegates. Qualifications Applicants should: (a) have a PhD or a doctoral degree, preferably in
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writing; and (c) perform any other duties as assigned by the project leader, the Head of Unit or their delegates. Qualifications Applicants should have: (a) a PhD degree plus at least six years of
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wearable sensing devices and human-computer interaction systems; and (d) be able to fabricate multi-material and multi-functional magnetic fibers to construct a fiber-based actuating system for a wearable
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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