53 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" research jobs at HONG KONG BAPTIST UNIVERSITY
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of application may consider their applications unsuccessful. Details of the University’s Personal Information Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable
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Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable-environment.html#privacy-policy . The University reserves the right not to make an appointment for the post
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at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable-environment.html#privacy-policy . The University reserves the right not to make an appointment for the post advertised, and the appointment
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(25260475) Responsibilities: The Department is recruiting a scholar at the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related
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of EdTech, art-tech, interdisciplinary learning, cross-cultural studies, and performance and pedagogical research. A key component of the project includes a new 5-6 year music programme that includes an in
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unsuccessful. Details of the University's Personal Information Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable-environment.html#privacy-policy
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of the University's Personal Information Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable-environment.html#privacy-policy . The University reserves the right not
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of application may consider their applications unsuccessful. Details of the University’s Personal Information Collection Statement can be found at https://hro.hkbu.edu.hk/en/worklife-at-hkbu/employee-favourable
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the rank of Research Assistant Professor in computational mathematics, machine learning, scientific computing, statistics, and related areas. The appointee is expected to conduct high-impact research
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original