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Field
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artificial intelligence-based algorithms to optimise operation and predict anomalies in water distribution networks. The algorithms developed should identify patterns and anomalies that indicate the presence
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the evaluation process. Failure to indicate the competition reference, the lack of any of the documents required in the application, or the sending/delivery of the application to a location other than that
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methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system architecture, component selection, and
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Curriculum Vitae; ii) Documents proving the academic degrees obtained and their classification* (in the application phase may be replaced by a declaration of honour as stated in point no. 2). iii) Declaration
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based on signal processing and artificial intelligence. 5-D is part of an innovative, active, and multidisciplinary research group: The Centre for Elderly and Nursing Home Medicine (SEFAS), which has
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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understanding the molecular basis of brain signalling and neurological disorders. Previous experience in tissue culture and proteomics including data collection, processing and interpretation is essential
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appointees. The appointees will also be provided with research funding of HK$10,000 per year to support their research activities. Application Procedure Applicants are required to contact potential supervisors
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in signal representation/processing, esp for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state
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traditional assessment scales with sensor-based measurements, with novel analyses based on signal processing and artificial intelligence. 5-D is part of an innovative, active, and multidisciplinary research