364 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"FEUP" positions in Hong Kong
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(LLMs). Applicants are invited to contact Prof. Wu Yujie at telephone number 3400 8361 or via email at yu-jie.wu@polyu.edu.hk for further information. Conditions of Service A highly competitive
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post-qualification work experience; (b) have an excellent command of both written and spoken English and Chinese, with fluency in Putonghua being an advantage; (c) have good computer literacy
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. Qualifications Applicants should have: (a) a recognised degree or a higher degree in Data Science, Engineering or a related discipline; (b) at least five years of work experience in a field related to digital
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at telephone number 2766 7141 or via email at edward-yh.lai@polyu.edu.hk for further information. Conditions of Service A highly competitive remuneration package will be offered. The closing date
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efficient Federated Learning ecosystem; (f) lead cross-functional teams (including algorithm engineers, data scientists, product managers, development engineers, etc.) to define roles, formulate plans and
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evaluations, if any, and (7) contact information of at least three referees. Review of applications will start as soon as possible and continue until May 29, 2026 , or until the post is filled, whichever is
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Engineering or Data Science or a related discipline; (b) have at least five years of solid post-qualification experience in the relevant fields; (c) have a good command of both written and spoken English
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not less than three members; (c) proficiency in both written and spoken English and Putonghua; (d) strong computer literacy; and (e) excellent interpersonal and communication skills
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Tsang via email at albertiso.tsang@polyu.edu.hk for further information. Conditions of Service A highly competitive remuneration package will be offered. Consideration of applications will commence on 16
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Responsibilities Drive research activities towards defined goals and priorities within a specified timeline Perform advanced level of data analysis (quantitative and qualitative) Advance knowledge based on the data