97 big-data-and-machine-learning-phd Fellowship positions at Hong Kong Polytechnic University
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machine learning methods, particularly large language models (LLMs), to marketing research. Applicants are invited to contact Prof. Edward Lai at telephone number 2766 7141 or via email at edward-yh.lai
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the research projects - “Monitoring and predicting slope instability in forested terrain from multi-mode remote sensing data” and “Resilience of rural infrastructure and communities to climate change
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the research project - “Development and clinical investigation of an AI-empowered portable device for assessment of radiation dermatitis in cancer patients”. Qualifications Applicants should have a PhD degree in
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the research project - “Radiotheranostics: emerging integrated imaging and therapies for precision oncology”. Qualifications Applicants should have a PhD degree in Medical Physics, Physics, Biomedical
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. For all posts, applicants should also: (a) be proficient with machine learning toolsets such as CUDA and PyTorch; (b) have experience with game development platforms such as Unity or Unreal Engine; (c
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) liaise with collaborators for system development and data collection; (c) support on-site data collection; (d) carry out preliminary data analysis; (e) draft progress and final reports; and (f
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projects on-time with minimum supervision. Preference will be given to those with research experience in machines designs and development of computer programmes for numerical computation of electromagnetic
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- “The structural evolution of the Chinese aviation network during and after the pandemic: a machine learning-based approach”. He/she will be required to: (a) conduct high-quality research in the field
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the research project - “White adipose tissue (fat) dysfunction in ageing and its related metabolic diseases: new insight and therapeutic intervention”. Qualifications Applicants should have: (a) a PhD degree
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and energy materials. Preference will be given to those with knowledge of computer programming, AI or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766