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the public at large. Any other assigned duties Requirements: Candidates should be passionate about teaching and learning, hold a PhD or at least a Master's degree in related disciplines, and have
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will conduct the lab experiment for RAS system for pollution control in recycled water in aquaculture system. He/she will also use machine learning tools to predict and optimize the RAS system. Job
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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market trends, incorporating factors such as weather patterns, consumption behavior, and regulatory changes. By leveraging advanced statistical and machine learning techniques, the role aims to provide
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writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
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Requirements: Preferably PhD in computer science or related field. Expertise in computer programming Knowledge in machine learning Proven research ability as evidenced through a portfolio of publications and/or
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Investigator (PI) or team lead with project management tasks. Job Requirements: PhD degree in Optimization, Artificial Intelligence, Transportation or Aerospace. Evidence of developing Machine Learning and
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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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(CBmE) was established in September 2006 in the Yong Loo Lin School of Medicine through a generous gift by the Chen Su Lan Trust. CBmE, directed by Prof Julian Savulescu, is a thriving centre for learning
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diffusion models using path integral formulations. This project aims to advance quantum machine learning by: Designing a quantum counterpart of diffusion models; Leveraging path integral methods to model