102 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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nonlinear charge and spin transport effects”. Qualifications Applicants should have a PhD degree in Physics, Materials or Engineering or equivalent qualifications plus at least three years of postdoctoral
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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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of the project; (b) analyse qualitative research data; (c) assist in drafting of academic publications; (d) liaise with project partners and researchers; and (e) perform any other duties as assigned by
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- “Construct a large-scale low-cost solar water purification system to mass produce freshwater from seawater and wastewater”. Qualifications Applicants for the Research Fellow post should have a doctoral degree
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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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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
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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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twelve months] Duties The appointees will assist the project leader in the research project - “A multimodal intelligence-enabled strategy learning approach for cognitive human-robot collaborative assembly
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research using methods such as sensing technique, 3D printing, human-computer interaction, simulation, and/or machine learning to address challenges in machinery motion planning and construction safety