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experience in spatial analysis and/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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of machine learning, simulation-driven testing, and iterative calibration based on real-world datasets. Contribute to scholarly publications, technical documentation, and progress reports required by funding
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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requirements: PhD Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field, obtained within the last five year Research Experience in one or more of the following
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relevant data science and machine learning tools. Able to work independently and comfortably with a team and external/international collaborators. Able to handle multiple tasks relevant to both project and
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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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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Fellow in Education focusing on Collaborative Learning Apply for this job See advertisement Job description Applications are invited for a four-year position as PhD Research Fellow affiliated
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Applicants are invited for the posts of Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University of Manchester. You