42 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" research jobs in Hong Kong
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qualification; (b) be proficient in machine learning frameworks; (c) have strong background in AI modelling, quantitative data analysis, mathematical modelling, and experience in methodological development
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Science, Psychology, Statistics, Data Science, or a related field, or an equivalent qualification; (a) have strong training in econometrics and/or statistics; (b) have proficiency in machine learning
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- “Cascaded selective prediction for reliable LLM agents”. He/She will carry out research in the area of machine learning and data science, and also be required to: (a) develop an effective cascading
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in econometrics and/or statistics; (b) have proficiency in machine learning techniques and tools; (c) have experience working with large language models (e.g., GPT, BERT); (d) have hands
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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to formulate mathematical models of the problems and develop efficient solution methods, particularly by leveraging techniques from machine learning and operations research. b) The applicants are expected
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Department is available on the departmental website at www.polyu.edu.hk/hti/ . We are looking for a highly motivated personnel with expertise in bioinformatics, neuroimaging, machine learning, mental health
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of Artificial Intelligence (AI) into higher education. The appointee will play a key role in advancing PolyU’s strategic initiatives in digital transformation, teaching innovation and AI-enabled learning. Key
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Science, or an equivalent qualification; (b) experience in field measurements of food waste, bioconversion and machine learning modelling; (c) strong analytical and problem-solving skills, with
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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