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., MATLAB, Python) is required. Experience with machine learning is highly preferred. Ability to work independently and as part of a team. Key Requirements for PhD: Hold a Bachelor's degree with outstanding
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editing”. Qualifications Applicants for the Postdoctoral Fellow post should have a PhD degree in Computer Science, Electrical and Computer Engineering or a related discipline or an equivalent qualification
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teaching duties. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct
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Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition; and (iv) Distributed Systems and Networking. These key research areas have a special thematic focus on (a
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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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pillars such as Big Data Analytics and Management, Machine Learning and Optimization, AI for Data Science, Deep Learning, Generative Learning, Biometrics Processing, Natural Language Processing, Computer
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machine learning. There are currently 54 academic staff and about 105 research personnel in the Department. Please visit the website at https://www.polyu.edu.hk/ama for more information about the
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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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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities